| $\ell_1$-regression with Heavy-tailed Distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| (Probably) Concave Graph Matching |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| 3D-Aware Scene Manipulation via Inverse Graphics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Bandit Approach to Sequential Experimental Design with False Discovery Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Bayes-Sard Cubature Method |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Bayesian Approach to Generative Adversarial Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Bayesian Nonparametric View on Count-Min Sketch |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Block Coordinate Ascent Algorithm for Mean-Variance Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Bridging Framework for Model Optimization and Deep Propagation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| A Convex Duality Framework for GANs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Dual Framework for Low-rank Tensor Completion |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A General Method for Amortizing Variational Filtering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A Lyapunov-based Approach to Safe Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Mathematical Model For Optimal Decisions In A Representative Democracy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Model for Learned Bloom Filters and Optimizing by Sandwiching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Neural Compositional Paradigm for Image Captioning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Practical Algorithm for Distributed Clustering and Outlier Detection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Probabilistic U-Net for Segmentation of Ambiguous Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Reduction for Efficient LDA Topic Reconstruction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Retrieve-and-Edit Framework for Predicting Structured Outputs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Simple Cache Model for Image Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Smoother Way to Train Structured Prediction Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Spectral View of Adversarially Robust Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Statistical Recurrent Model on the Manifold of Symmetric Positive Definite Matrices |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Stein variational Newton method |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Structured Prediction Approach for Label Ranking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Unified Framework for Extensive-Form Game Abstraction with Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Unified View of Piecewise Linear Neural Network Verification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A convex program for bilinear inversion of sparse vectors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A flexible model for training action localization with varying levels of supervision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A loss framework for calibrated anomaly detection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A no-regret generalization of hierarchical softmax to extreme multi-label classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A probabilistic population code based on neural samples |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A theory on the absence of spurious solutions for nonconvex and nonsmooth optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| ATOMO: Communication-efficient Learning via Atomic Sparsification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A^2-Nets: Double Attention Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Acceleration through Optimistic No-Regret Dynamics |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Active Learning for Non-Parametric Regression Using Purely Random Trees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Active Matting |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Actor-Critic Policy Optimization in Partially Observable Multiagent Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptation to Easy Data in Prediction with Limited Advice |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Learning with Unknown Information Flows |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Methods for Nonconvex Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Negative Curvature Descent with Applications in Non-convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Online Learning in Dynamic Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Path-Integral Autoencoders: Representation Learning and Planning for Dynamical Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive Sampling Towards Fast Graph Representation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adding One Neuron Can Eliminate All Bad Local Minima |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adversarial Attacks on Stochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Examples that Fool both Computer Vision and Time-Limited Humans |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Multiple Source Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Regularizers in Inverse Problems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Adversarial Risk and Robustness: General Definitions and Implications for the Uniform Distribution |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Adversarial Scene Editing: Automatic Object Removal from Weak Supervision |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Text Generation via Feature-Mover's Distance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial vulnerability for any classifier |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarially Robust Generalization Requires More Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarially Robust Optimization with Gaussian Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Algebraic tests of general Gaussian latent tree models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Algorithmic Linearly Constrained Gaussian Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Algorithms and Theory for Multiple-Source Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Alternating optimization of decision trees, with application to learning sparse oblique trees |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Amortized Inference Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Efficient Pruning Algorithm for Robust Isotonic Regression |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Information-Theoretic Analysis for Thompson Sampling with Many Actions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Off-policy Policy Gradient Theorem Using Emphatic Weightings |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An intriguing failing of convolutional neural networks and the CoordConv solution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Analysis of Krylov Subspace Solutions of Regularized Non-Convex Quadratic Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate Knowledge Compilation by Online Collapsed Importance Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Approximating Real-Time Recurrent Learning with Random Kronecker Factors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Approximation algorithms for stochastic clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Are GANs Created Equal? A Large-Scale Study |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Are ResNets Provably Better than Linear Predictors? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Assessing Generative Models via Precision and Recall |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Asymptotic optimality of adaptive importance sampling |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Attention in Convolutional LSTM for Gesture Recognition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatic Program Synthesis of Long Programs with a Learned Garbage Collector |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Automatic differentiation in ML: Where we are and where we should be going |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Automating Bayesian optimization with Bayesian optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| BRITS: Bidirectional Recurrent Imputation for Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BRUNO: A Deep Recurrent Model for Exchangeable Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Backpropagation with Callbacks: Foundations for Efficient and Expressive Differentiable Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Balanced Policy Evaluation and Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Banach Wasserstein GAN |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bandit Learning in Concave N-Person Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandit Learning with Implicit Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bandit Learning with Positive Externalities |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Adversarial Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Alignments of Warped Multi-Output Gaussian Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bayesian Control of Large MDPs with Unknown Dynamics in Data-Poor Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Distributed Stochastic Gradient Descent |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Bayesian Inference of Temporal Task Specifications from Demonstrations |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Model-Agnostic Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bayesian Nonparametric Spectral Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian Semi-supervised Learning with Graph Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bayesian Structure Learning by Recursive Bootstrap |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Beauty-in-averageness and its contextual modulations: A Bayesian statistical account |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Benefits of over-parameterization with EM |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Beyond Grids: Learning Graph Representations for Visual Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bias and Generalization in Deep Generative Models: An Empirical Study |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bilevel Distance Metric Learning for Robust Image Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bilevel learning of the Group Lasso structure |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bilinear Attention Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| BinGAN: Learning Compact Binary Descriptors with a Regularized GAN |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Binary Classification from Positive-Confidence Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Binary Rating Estimation with Graph Side Information |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bipartite Stochastic Block Models with Tiny Clusters |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Blind Deconvolutional Phase Retrieval via Convex Programming |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Blockwise Parallel Decoding for Deep Autoregressive Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Boolean Decision Rules via Column Generation |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Boosted Sparse and Low-Rank Tensor Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Boosting Black Box Variational Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bounded-Loss Private Prediction Markets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| BourGAN: Generative Networks with Metric Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Breaking the Activation Function Bottleneck through Adaptive Parameterization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Breaking the Span Assumption Yields Fast Finite-Sum Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| But How Does It Work in Theory? Linear SVM with Random Features |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Byzantine Stochastic Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| COLA: Decentralized Linear Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Can We Gain More from Orthogonality Regularizations in Training Deep Networks? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| CatBoost: unbiased boosting with categorical features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Causal Discovery from Discrete Data using Hidden Compact Representation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Inference and Mechanism Clustering of A Mixture of Additive Noise Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causal Inference via Kernel Deviance Measures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Causal Inference with Noisy and Missing Covariates via Matrix Factorization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Chain of Reasoning for Visual Question Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Chaining Mutual Information and Tightening Generalization Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Co-regularized Alignment for Unsupervised Domain Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Co-teaching: Robust training of deep neural networks with extremely noisy labels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Collaborative Learning for Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Communication Compression for Decentralized Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Communication Efficient Parallel Algorithms for Optimization on Manifolds |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Community Exploration: From Offline Optimization to Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Compact Generalized Non-local Network |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compact Representation of Uncertainty in Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Completing State Representations using Spectral Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Complex Gated Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Computationally and statistically efficient learning of causal Bayes nets using path queries |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Computing Higher Order Derivatives of Matrix and Tensor Expressions |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Conditional Adversarial Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Confounding-Robust Policy Improvement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Connecting Optimization and Regularization Paths |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Connectionist Temporal Classification with Maximum Entropy Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Constant Regret, Generalized Mixability, and Mirror Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constrained Cross-Entropy Method for Safe Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Constrained Graph Variational Autoencoders for Molecule Design |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constructing Deep Neural Networks by Bayesian Network Structure Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Constructing Fast Network through Deconstruction of Convolution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Constructing Unrestricted Adversarial Examples with Generative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contamination Attacks and Mitigation in Multi-Party Machine Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Content preserving text generation with attribute controls |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Context-aware Synthesis and Placement of Object Instances |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Context-dependent upper-confidence bounds for directed exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Contextual Pricing for Lipschitz Buyers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Stochastic Block Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Contextual bandits with surrogate losses: Margin bounds and efficient algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contour location via entropy reduction leveraging multiple information sources |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Contrastive Learning from Pairwise Measurements |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence of Cubic Regularization for Nonconvex Optimization under KL Property |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convex Elicitation of Continuous Properties |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Cooperative Holistic Scene Understanding: Unifying 3D Object, Layout, and Camera Pose Estimation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Coordinate Descent with Bandit Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Coupled Variational Bayes via Optimization Embedding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Credit Assignment For Collective Multiagent RL With Global Rewards |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Critical initialisation for deep signal propagation in noisy rectifier neural networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DAGs with NO TEARS: Continuous Optimization for Structure Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data Amplification: A Unified and Competitive Approach to Property Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data center cooling using model-predictive control |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Data-Driven Clustering via Parameterized Lloyd's Families |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data-Efficient Hierarchical Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data-dependent PAC-Bayes priors via differential privacy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Anomaly Detection Using Geometric Transformations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Attentive Tracking via Reciprocative Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Defense: Training DNNs with Improved Adversarial Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Dynamical Modeling and Control of Unsteady Fluid Flows |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Generative Markov State Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Deep Generative Models for Distribution-Preserving Lossy Compression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Generative Models with Learnable Knowledge Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep Homogeneous Mixture Models: Representation, Separation, and Approximation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Neural Nets with Interpolating Function as Output Activation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Neural Networks with Box Convolutions |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Poisson gamma dynamical systems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Reinforcement Learning of Marked Temporal Point Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep State Space Models for Time Series Forecasting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep State Space Models for Unconditional Word Generation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Structured Prediction with Nonlinear Output Transformations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DeepExposure: Learning to Expose Photos with Asynchronously Reinforced Adversarial Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DeepPINK: reproducible feature selection in deep neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DeepProbLog: Neural Probabilistic Logic Programming |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deepcode: Feedback Codes via Deep Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Delta-encoder: an effective sample synthesis method for few-shot object recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dendritic cortical microcircuits approximate the backpropagation algorithm |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Densely Connected Attention Propagation for Reading Comprehension |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Depth-Limited Solving for Imperfect-Information Games |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Derivative Estimation in Random Design |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dialog-based Interactive Image Retrieval |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dialog-to-Action: Conversational Question Answering Over a Large-Scale Knowledge Base |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DifNet: Semantic Segmentation by Diffusion Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Differentiable MPC for End-to-end Planning and Control |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differential Privacy for Growing Databases |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentially Private Bayesian Inference for Exponential Families |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Differentially Private Change-Point Detection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Differentially Private Contextual Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Robust Low-Rank Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Testing of Identity and Closeness of Discrete Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Uniformly Most Powerful Tests for Binomial Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Differentially Private k-Means with Constant Multiplicative Error |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Diffusion Maps for Textual Network Embedding |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diminishing Returns Shape Constraints for Interpretability and Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Direct Estimation of Differences in Causal Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Direct Runge-Kutta Discretization Achieves Acceleration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dirichlet belief networks for topic structure learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Disconnected Manifold Learning for Generative Adversarial Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discretely Relaxing Continuous Variables for tractable Variational Inference |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discrimination-aware Channel Pruning for Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Distilled Wasserstein Learning for Word Embedding and Topic Modeling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributed $k$-Clustering for Data with Heavy Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Multi-Player Bandits - a Game of Thrones Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributed Multitask Reinforcement Learning with Quadratic Convergence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributed Stochastic Optimization via Adaptive SGD |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Distributed Weight Consolidation: A Brain Segmentation Case Study |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributionally Robust Graphical Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Diverse Ensemble Evolution: Curriculum Data-Model Marriage |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diversity-Driven Exploration Strategy for Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Do Less, Get More: Streaming Submodular Maximization with Subsampling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Does mitigating ML's impact disparity require treatment disparity? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Domain-Invariant Projection Learning for Zero-Shot Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $\beta$-Divergences |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DropBlock: A regularization method for convolutional networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DropMax: Adaptive Variational Softmax |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Dual Policy Iteration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dual Swap Disentangling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamic Network Model from Partial Observations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Early Stopping for Nonparametric Testing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Anomaly Detection via Matrix Sketching |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Formal Safety Analysis of Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Loss-Based Decoding on Graphs for Extreme Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Neural Network Robustness Certification with General Activation Functions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Efficient Online Portfolio with Logarithmic Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Projection onto the Perfect Phylogeny Model |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Efficient Stochastic Gradient Hard Thresholding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient inference for time-varying behavior during learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient nonmyopic batch active search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient online algorithms for fast-rate regret bounds under sparsity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Embedding Logical Queries on Knowledge Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Empirical Risk Minimization Under Fairness Constraints |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| End-to-End Differentiable Physics for Learning and Control |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Enhancing the Accuracy and Fairness of Human Decision Making |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Entropy Rate Estimation for Markov Chains with Large State Space |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Entropy and mutual information in models of deep neural networks |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Equality of Opportunity in Classification: A Causal Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Escaping Saddle Points in Constrained Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating Learnability in the Sublinear Data Regime |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Estimators for Multivariate Information Measures in General Probability Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Evidential Deep Learning to Quantify Classification Uncertainty |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evolution-Guided Policy Gradient in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evolved Policy Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Ex ante coordination and collusion in zero-sum multi-player extensive-form games |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Exact natural gradient in deep linear networks and its application to the nonlinear case |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Expanding Holographic Embeddings for Knowledge Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Experimental Design for Cost-Aware Learning of Causal Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Explaining Deep Learning Models -- A Bayesian Non-parametric Approach |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exploration in Structured Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exponentially Weighted Imitation Learning for Batched Historical Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exponentiated Strongly Rayleigh Distributions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Extracting Relationships by Multi-Domain Matching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FRAGE: Frequency-Agnostic Word Representation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Factored Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fairness Through Computationally-Bounded Awareness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faithful Inversion of Generative Models for Effective Amortized Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Estimation of Causal Interactions using Wold Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Fast Similarity Search via Optimal Sparse Lifting |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Effective Robustness Certification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast deep reinforcement learning using online adjustments from the past |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast greedy algorithms for dictionary selection with generalized sparsity constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Faster Neural Networks Straight from JPEG |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Faster Online Learning of Optimal Threshold for Consistent F-measure Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fighting Boredom in Recommender Systems with Linear Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Flexible and accurate inference and learning for deep generative models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flexible neural representation for physics prediction |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Foreground Clustering for Joint Segmentation and Localization in Videos and Images |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Found Graph Data and Planted Vertex Covers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Frequency-Domain Dynamic Pruning for Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| From Stochastic Planning to Marginal MAP |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fully Understanding The Hashing Trick |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| GIANT: Globally Improved Approximate Newton Method for Distributed Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GILBO: One Metric to Measure Them All |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GLoMo: Unsupervised Learning of Transferable Relational Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gaussian Process Conditional Density Estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Gaussian Process Prior Variational Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalisation in humans and deep neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Generalisation of structural knowledge in the hippocampal-entorhinal system |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Generalization Bounds for Uniformly Stable Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized Inverse Optimization through Online Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Generalized Zero-Shot Learning with Deep Calibration Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalizing Graph Matching beyond Quadratic Assignment Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalizing Tree Probability Estimation via Bayesian Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalizing to Unseen Domains via Adversarial Data Augmentation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Generative Neural Machine Translation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Probabilistic Novelty Detection with Adversarial Autoencoders |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generative modeling for protein structures |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
2 |
| Genetic-Gated Networks for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Geometrically Coupled Monte Carlo Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geometry Based Data Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Geometry-Aware Recurrent Neural Networks for Active Visual Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Non-convex Optimization with Discretized Diffusions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Glow: Generative Flow with Invertible 1x1 Convolutions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient Descent for Spiking Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gradient Sparsification for Communication-Efficient Distributed Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Graphical Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Graphical model inference: Sequential Monte Carlo meets deterministic approximations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Group Equivariant Capsule Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GumBolt: Extending Gumbel trick to Boltzmann priors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| HOGWILD!-Gibbs can be PanAccurate |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| HOUDINI: Lifelong Learning as Program Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Hamiltonian Variational Auto-Encoder |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hardware Conditioned Policies for Multi-Robot Transfer Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hessian-based Analysis of Large Batch Training and Robustness to Adversaries |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Heterogeneous Bitwidth Binarization in Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Heterogeneous Multi-output Gaussian Process Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Graph Representation Learning with Differentiable Pooling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| High Dimensional Linear Regression using Lattice Basis Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| HitNet: Hybrid Ternary Recurrent Neural Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Horizon-Independent Minimax Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| How Does Batch Normalization Help Optimization? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How Many Samples are Needed to Estimate a Convolutional Neural Network? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| How to Start Training: The Effect of Initialization and Architecture |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How to tell when a clustering is (approximately) correct using convex relaxations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Human-in-the-Loop Interpretability Prior |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Hunting for Discriminatory Proxies in Linear Regression Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hybrid Knowledge Routed Modules for Large-scale Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Hyperbolic Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identification and Estimation of Causal Effects from Dependent Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Image Inpainting via Generative Multi-column Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Image-to-image translation for cross-domain disentanglement |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Implicit Bias of Gradient Descent on Linear Convolutional Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Implicit Probabilistic Integrators for ODEs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Reparameterization Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Importance Weighting and Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Improved Algorithms for Collaborative PAC Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Expressivity Through Dendritic Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Network Robustness with Adversary Critic |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Explorability in Variational Inference with Annealed Variational Objectives |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Neural Program Synthesis with Inferred Execution Traces |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Improving Online Algorithms via ML Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Simple Models with Confidence Profiles |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Incorporating Context into Language Encoding Models for fMRI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inequity aversion improves cooperation in intertemporal social dilemmas |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inexact trust-region algorithms on Riemannian manifolds |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inferring Latent Velocities from Weather Radar Data using Gaussian Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inferring Networks From Random Walk-Based Node Similarities |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Infinite-Horizon Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Information Constraints on Auto-Encoding Variational Bayes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information-theoretic Limits for Community Detection in Network Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Informative Features for Model Comparison |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Insights on representational similarity in neural networks with canonical correlation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interactive Structure Learning with Structural Query-by-Committee |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Invariant Representations without Adversarial Training |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Invertibility of Convolutional Generative Networks from Partial Measurements |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Is Q-Learning Provably Efficient? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Isolating Sources of Disentanglement in Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Iterative Value-Aware Model Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Joint Autoregressive and Hierarchical Priors for Learned Image Compression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| KDGAN: Knowledge Distillation with Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| KONG: Kernels for ordered-neighborhood graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kalman Normalization: Normalizing Internal Representations Across Network Layers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Knowledge Distillation by On-the-Fly Native Ensemble |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| L4: Practical loss-based stepsize adaptation for deep learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LF-Net: Learning Local Features from Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Margin Deep Networks for Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Large-Scale Stochastic Sampling from the Probability Simplex |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Latent Alignment and Variational Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Abstract Options |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Attentional Communication for Multi-Agent Cooperation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Attractor Dynamics for Generative Memory |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Beam Search Policies via Imitation Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Compressed Transforms with Low Displacement Rank |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning Conditioned Graph Structures for Interpretable Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Confidence Sets using Support Vector Machines |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Deep Disentangled Embeddings With the F-Statistic Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Disentangled Joint Continuous and Discrete Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Hierarchical Semantic Image Manipulation through Structured Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Invariances using the Marginal Likelihood |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Latent Subspaces in Variational Autoencoders |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Loop Invariants for Program Verification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learning Optimal Reserve Price against Non-myopic Bidders |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Plannable Representations with Causal InfoGAN |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning SMaLL Predictors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Safe Policies with Expert Guidance |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Task Specifications from Demonstrations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Temporal Point Processes via Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning To Learn Around A Common Mean |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Versatile Filters for Efficient Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning a latent manifold of odor representations from neural responses in piriform cortex |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning and Inference in Hilbert Space with Quantum Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning and Testing Causal Models with Interventions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning convex bounds for linear quadratic control policy synthesis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning convex polytopes with margin |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning filter widths of spectral decompositions with wavelets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning from Group Comparisons: Exploiting Higher Order Interactions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning from discriminative feature feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning in Games with Lossy Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning latent variable structured prediction models with Gaussian perturbations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning long-range spatial dependencies with horizontal gated recurrent units |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning semantic similarity in a continuous space |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning sparse neural networks via sensitivity-driven regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Decompose and Disentangle Representations for Video Prediction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning to Exploit Stability for 3D Scene Parsing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Infer Graphics Programs from Hand-Drawn Images |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learning to Multitask |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Navigate in Cities Without a Map |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Optimize Tensor Programs |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learning to Play With Intrinsically-Motivated, Self-Aware Agents |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning to Reason with Third Order Tensor Products |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Reconstruct Shapes from Unseen Classes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Repair Software Vulnerabilities with Generative Adversarial Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Share and Hide Intentions using Information Regularization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Solve SMT Formulas |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Specialize with Knowledge Distillation for Visual Question Answering |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Teach with Dynamic Loss Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning towards Minimum Hyperspherical Energy |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning with SGD and Random Features |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning without the Phase: Regularized PhaseMax Achieves Optimal Sample Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Legendre Decomposition for Tensors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Leveraged volume sampling for linear regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Leveraging the Exact Likelihood of Deep Latent Variable Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Lifelong Inverse Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Lifted Weighted Mini-Bucket |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Link Prediction Based on Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LinkNet: Relational Embedding for Scene Graph |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lipschitz regularity of deep neural networks: analysis and efficient estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Local Differential Privacy for Evolving Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Long short-term memory and Learning-to-learn in networks of spiking neurons |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Loss Functions for Multiset Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mallows Models for Top-k Lists |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Manifold Structured Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masking: A New Perspective of Noisy Supervision |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Maximizing Induced Cardinality Under a Determinantal Point Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximizing acquisition functions for Bayesian optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Maximum Causal Tsallis Entropy Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Maximum-Entropy Fine Grained Classification |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Mean-field theory of graph neural networks in graph partitioning |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
3 |
| Measures of distortion for machine learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Memory Replay GANs: Learning to Generate New Categories without Forgetting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mental Sampling in Multimodal Representations |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mesh-TensorFlow: Deep Learning for Supercomputers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta-Gradient Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Learning MCMC Proposals |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Reinforcement Learning of Structured Exploration Strategies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MetaAnchor: Learning to Detect Objects with Customized Anchors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MetaGAN: An Adversarial Approach to Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MetaReg: Towards Domain Generalization using Meta-Regularization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Metric on Nonlinear Dynamical Systems with Perron-Frobenius Operators |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Middle-Out Decoding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Minimax Estimation of Neural Net Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Statistical Learning with Wasserstein distances |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mirrored Langevin Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mixture Matrix Completion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Agnostic Supervised Local Explanations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model-Agnostic Private Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model-based targeted dimensionality reduction for neuronal population data |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modeling Dynamic Missingness of Implicit Feedback for Recommendation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Modelling and unsupervised learning of symmetric deformable object categories |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modern Neural Networks Generalize on Small Data Sets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modular Networks: Learning to Decompose Neural Computation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Monte-Carlo Tree Search for Constrained POMDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Moonshine: Distilling with Cheap Convolutions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent Generative Adversarial Imitation Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Class Learning: From Theory to Algorithm |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Multi-Layered Gradient Boosting Decision Trees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Task Learning as Multi-Objective Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Multi-Task Zipping via Layer-wise Neuron Sharing |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Multi-armed Bandits with Compensation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-domain Causal Structure Learning in Linear Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multimodal Generative Models for Scalable Weakly-Supervised Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multitask Boosting for Survival Analysis with Competing Risks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multivariate Time Series Imputation with Generative Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| NAIS-Net: Stable Deep Networks from Non-Autonomous Differential Equations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NEON2: Finding Local Minima via First-Order Oracles |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Natasha 2: Faster Non-Convex Optimization Than SGD |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neighbourhood Consensus Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Architecture Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Architecture Search with Bayesian Optimisation and Optimal Transport |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Arithmetic Logic Units |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Code Comprehension: A Learnable Representation of Code Semantics |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Edit Operations for Biological Sequences |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Neural Guided Constraint Logic Programming for Program Synthesis |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neural Interaction Transparency (NIT): Disentangling Learned Interactions for Improved Interpretability |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Nearest Neighbors Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Networks Trained to Solve Differential Equations Learn General Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Ordinary Differential Equations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Proximal Gradient Descent for Compressive Imaging |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Tangent Kernel: Convergence and Generalization in Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Voice Cloning with a Few Samples |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Non-Adversarial Mapping with VAEs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Non-Ergodic Alternating Proximal Augmented Lagrangian Algorithms with Optimal Rates |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Non-Local Recurrent Network for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Non-delusional Q-learning and value-iteration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-metric Similarity Graphs for Maximum Inner Product Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Non-monotone Submodular Maximization in Exponentially Fewer Iterations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Nonparametric Density Estimation under Adversarial Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric learning from Bayesian models with randomized objective functions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Norm matters: efficient and accurate normalization schemes in deep networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Norm-Ranging LSH for Maximum Inner Product Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Object-Oriented Dynamics Predictor |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Objective and efficient inference for couplings in neuronal networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Occam's razor is insufficient to infer the preferences of irrational agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Binary Classification in Extreme Regions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Controllable Sparse Alternatives to Softmax |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Coresets for Logistic Regression |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On Fast Leverage Score Sampling and Optimal Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On GANs and GMMs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Learning Intrinsic Rewards for Policy Gradient Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Learning Markov Chains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Markov Chain Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Misinformation Containment in Online Social Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Neuronal Capacity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Oracle-Efficient PAC RL with Rich Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On gradient regularizers for MMD GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On preserving non-discrimination when combining expert advice |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Convergence and Robustness of Training GANs with Regularized Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Dimensionality of Word Embedding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Local Hessian in Back-propagation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Local Minima of the Empirical Risk |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| One-Shot Unsupervised Cross Domain Translation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Adaptive Methods, Universality and Acceleration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Improper Learning with an Approximation Oracle |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning of Quantum States |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning with an Unknown Fairness Metric |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Reciprocal Recommendation with Theoretical Performance Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Robust Policy Learning in the Presence of Unknown Adversaries |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online convex optimization for cumulative constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Algorithms for Continuous Non-monotone Submodular and DR-Submodular Maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Algorithms for Non-Smooth Distributed Optimization in Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Subsampling with Influence Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Optimistic optimization of a Brownian |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization for Approximate Submodularity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimization over Continuous and Multi-dimensional Decisions with Observational Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Orthogonally Decoupled Variational Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Out-of-Distribution Detection using Multiple Semantic Label Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Overcoming Language Priors in Visual Question Answering with Adversarial Regularization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Overlapping Clustering Models, and One (class) SVM to Bind Them All |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| PAC-Bayes Tree: weighted subtrees with guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| PAC-Bayes bounds for stable algorithms with instance-dependent priors |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PAC-learning in the presence of adversaries |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| PCA of high dimensional random walks with comparison to neural network training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PacGAN: The power of two samples in generative adversarial networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Parameters as interacting particles: long time convergence and asymptotic error scaling of neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Paraphrasing Complex Network: Network Compression via Factor Transfer |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Parsimonious Bayesian deep networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Partially-Supervised Image Captioning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pelee: A Real-Time Object Detection System on Mobile Devices |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Phase Retrieval Under a Generative Prior |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Playing hard exploration games by watching YouTube |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Point process latent variable models of larval zebrafish behavior |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| PointCNN: Convolution On X-Transformed Points |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Policy Optimization via Importance Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Policy Regret in Repeated Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Porcupine Neural Networks: Approximating Neural Network Landscapes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Posterior Concentration for Sparse Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Power-law efficient neural codes provide general link between perceptual bias and discriminability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Practical Methods for Graph Two-Sample Testing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Practical exact algorithm for trembling-hand equilibrium refinements in games |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Precision and Recall for Time Series |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predictive Approximate Bayesian Computation via Saddle Points |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Predictive Uncertainty Estimation via Prior Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Preference Based Adaptation for Learning Objectives |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Probabilistic Matrix Factorization for Automated Machine Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Model-Agnostic Meta-Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Probabilistic Neural Programmed Networks for Scene Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Processing of missing data by neural networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provable Gaussian Embedding with One Observation |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Provable Variational Inference for Constrained Log-Submodular Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Correct Automatic Sub-Differentiation for Qualified Programs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proximal Graphical Event Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Proximal SCOPE for Distributed Sparse Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Q-learning with Nearest Neighbors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quadratic Decomposable Submodular Function Minimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quadrature-based features for kernel approximation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quantifying Learning Guarantees for Convex but Inconsistent Surrogates |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Query Complexity of Bayesian Private Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Query K-means Clustering and the Double Dixie Cup Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| REFUEL: Exploring Sparse Features in Deep Reinforcement Learning for Fast Disease Diagnosis |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Random Feature Stein Discrepancies |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Randomized Prior Functions for Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Re-evaluating evaluation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Realistic Evaluation of Deep Semi-Supervised Learning Algorithms |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rectangular Bounding Process |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recurrent Relational Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recurrent Transformer Networks for Semantic Correspondence |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Recurrent World Models Facilitate Policy Evolution |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recurrently Controlled Recurrent Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reducing Network Agnostophobia |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Regret Bounds for Online Portfolio Selection with a Cardinality Constraint |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularization Learning Networks: Deep Learning for Tabular Datasets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Regularizing by the Variance of the Activations' Sample-Variances |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reinforced Continual Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Reinforcement Learning for Solving the Vehicle Routing Problem |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Reinforcement Learning of Theorem Proving |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relating Leverage Scores and Density using Regularized Christoffel Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Relational recurrent neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Removing Hidden Confounding by Experimental Grounding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Removing the Feature Correlation Effect of Multiplicative Noise |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| RenderNet: A deep convolutional network for differentiable rendering from 3D shapes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reparameterization Gradient for Non-differentiable Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representation Balancing MDPs for Off-policy Policy Evaluation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representation Learning for Treatment Effect Estimation from Observational Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Representation Learning of Compositional Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representer Point Selection for Explaining Deep Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ResNet with one-neuron hidden layers is a Universal Approximator |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RetGK: Graph Kernels based on Return Probabilities of Random Walks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reversible Recurrent Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Revisiting Decomposable Submodular Function Minimization with Incidence Relations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reward learning from human preferences and demonstrations in Atari |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Hypothesis Testing Using Wasserstein Uncertainty Sets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Learning of Fixed-Structure Bayesian Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Subspace Approximation in a Stream |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Robustness of conditional GANs to noisy labels |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| SEGA: Variance Reduction via Gradient Sketching |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SING: Symbol-to-Instrument Neural Generator |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SLAYER: Spike Layer Error Reassignment in Time |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SNIPER: Efficient Multi-Scale Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Safe Active Learning for Time-Series Modeling with Gaussian Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Sanity Checks for Saliency Maps |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scalable Coordinated Exploration in Concurrent Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Scalable Hyperparameter Transfer Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable Laplacian K-modes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scalable Robust Matrix Factorization with Nonconvex Loss |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable methods for 8-bit training of neural networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalar Posterior Sampling with Applications |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Gaussian Process Regression with Derivatives |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Scaling provable adversarial defenses |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling the Poisson GLM to massive neural datasets through polynomial approximations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Searching for Efficient Multi-Scale Architectures for Dense Image Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| See and Think: Disentangling Semantic Scene Completion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Erasing Network for Integral Object Attention |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Generation of Spatial Audio for 360° Video |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-Supervised Learning with Declaratively Specified Entropy Constraints |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semi-crowdsourced Clustering with Deep Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semidefinite relaxations for certifying robustness to adversarial examples |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sequence-to-Segment Networks for Segment Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sequential Context Encoding for Duplicate Removal |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sharp Bounds for Generalized Uniformity Testing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sigsoftmax: Reanalysis of the Softmax Bottleneck |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SimplE Embedding for Link Prediction in Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Simple random search of static linear policies is competitive for reinforcement learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Simple, Distributed, and Accelerated Probabilistic Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Single-Agent Policy Tree Search With Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Size-Noise Tradeoffs in Generative Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sketching Method for Large Scale Combinatorial Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Smoothed analysis of the low-rank approach for smooth semidefinite programs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Snap ML: A Hierarchical Framework for Machine Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Solving Large Sequential Games with the Excessive Gap Technique |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Solving Non-smooth Constrained Programs with Lower Complexity than $\mathcal{O}(1/\varepsilon)$: A Primal-Dual Homotopy Smoothing Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse DNNs with Improved Adversarial Robustness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparse PCA from Sparse Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sparsified SGD with Memory |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Speaker-Follower Models for Vision-and-Language Navigation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Spectral Filtering for General Linear Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Signatures in Backdoor Attacks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SplineNets: Continuous Neural Decision Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical and Computational Trade-Offs in Kernel K-Means |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Statistical mechanics of low-rank tensor decomposition |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stein Variational Gradient Descent as Moment Matching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Step Size Matters in Deep Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stimulus domain transfer in recurrent models for large scale cortical population prediction on video |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stochastic Chebyshev Gradient Descent for Spectral Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Cubic Regularization for Fast Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Expectation Maximization with Variance Reduction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stochastic Nested Variance Reduction for Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Stochastic Nonparametric Event-Tensor Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Spectral and Conjugate Descent Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Streamlining Variational Inference for Constraint Satisfaction Problems |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Structural Causal Bandits: Where to Intervene? |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structure-Aware Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Structured Local Minima in Sparse Blind Deconvolution |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sublinear Time Low-Rank Approximation of Distance Matrices |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Submodular Field Grammars: Representation, Inference, and Application to Image Parsing |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Submodular Maximization via Gradient Ascent: The Case of Deep Submodular Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Supervised autoencoders: Improving generalization performance with unsupervised regularizers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Supervising Unsupervised Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Symbolic Graph Reasoning Meets Convolutions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Synaptic Strength For Convolutional Neural Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Synthesized Policies for Transfer and Adaptation across Tasks and Environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| TADAM: Task dependent adaptive metric for improved few-shot learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TETRIS: TilE-matching the TRemendous Irregular Sparsity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Task-Driven Convolutional Recurrent Models of the Visual System |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Teaching Inverse Reinforcement Learners via Features and Demonstrations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporal Regularization for Markov Decision Process |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Temporal alignment and latent Gaussian process factor inference in population spike trains |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Testing for Families of Distributions via the Fourier Transform |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Cluster Description Problem - Complexity Results, Formulations and Approximations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| The Convergence of Sparsified Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Description Length of Deep Learning models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Effect of Network Width on the Performance of Large-batch Training |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| The Everlasting Database: Statistical Validity at a Fair Price |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Global Anchor Method for Quantifying Linguistic Shifts and Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| The Importance of Sampling inMeta-Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Limits of Post-Selection Generalization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Lingering of Gradients: How to Reuse Gradients Over Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Physical Systems Behind Optimization Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Price of Fair PCA: One Extra dimension |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Price of Privacy for Low-rank Factorization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Sample Complexity of Semi-Supervised Learning with Nonparametric Mixture Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Sparse Manifold Transform |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The challenge of realistic music generation: modelling raw audio at scale |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The committee machine: Computational to statistical gaps in learning a two-layers neural network |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| The emergence of multiple retinal cell types through efficient coding of natural movies |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The promises and pitfalls of Stochastic Gradient Langevin Dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The streaming rollout of deep networks - towards fully model-parallel execution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Theoretical guarantees for EM under misspecified Gaussian mixture models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Thwarting Adversarial Examples: An $L_0$-Robust Sparse Fourier Transform |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tight Bounds for Collaborative PAC Learning via Multiplicative Weights |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| To Trust Or Not To Trust A Classifier |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Toddler-Inspired Visual Object Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| TopRank: A practical algorithm for online stochastic ranking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Topkapi: Parallel and Fast Sketches for Finding Top-K Frequent Elements |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Total stochastic gradient algorithms and applications in reinforcement learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Deep Conversational Recommendations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Towards Robust Detection of Adversarial Examples |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Robust Interpretability with Self-Explaining Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Text Generation with Adversarially Learned Neural Outlines |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Trading robust representations for sample complexity through self-supervised visual experience |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Training DNNs with Hybrid Block Floating Point |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Training Deep Models Faster with Robust, Approximate Importance Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Deep Neural Networks with 8-bit Floating Point Numbers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Neural Networks Using Features Replay |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Training deep learning based denoisers without ground truth data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Trajectory Convolution for Action Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transfer Learning with Neural AutoML |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transfer of Deep Reactive Policies for MDP Planning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transfer of Value Functions via Variational Methods |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Tree-to-tree Neural Networks for Program Translation |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Turbo Learning for CaptionBot and DrawingBot |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Uncertainty-Aware Attention for Reliable Interpretation and Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Understanding Batch Normalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding Regularized Spectral Clustering via Graph Conductance |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Uniform Convergence of Gradients for Non-Convex Learning and Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universal Growth in Production Economies |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unorganized Malicious Attacks Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Adversarial Invariance |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Attention-guided Image-to-Image Translation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Depth Estimation, 3D Face Rotation and Replacement |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Unsupervised Learning of Artistic Styles with Archetypal Style Analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Learning of Object Landmarks through Conditional Image Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Learning of Shape and Pose with Differentiable Point Clouds |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Learning of View-invariant Action Representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Text Style Transfer using Language Models as Discriminators |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Video Object Segmentation for Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Uplift Modeling from Separate Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Using Large Ensembles of Control Variates for Variational Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Variance-Reduced Stochastic Gradient Descent on Streaming Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Bayesian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variational Inference with Tail-adaptive f-Divergence |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Learning on Aggregate Outputs with Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Memory Encoder-Decoder |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Verifiable Reinforcement Learning via Policy Extraction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Video Prediction via Selective Sampling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Video-to-Video Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| VideoCapsuleNet: A Simplified Network for Action Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Virtual Class Enhanced Discriminative Embedding Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Visual Memory for Robust Path Following |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Visual Object Networks: Image Generation with Disentangled 3D Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Visual Reinforcement Learning with Imagined Goals |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Visualizing the Loss Landscape of Neural Nets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Wasserstein Distributionally Robust Kalman Filtering |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Wasserstein Variational Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Watch Your Step: Learning Node Embeddings via Graph Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Wavelet regression and additive models for irregularly spaced data |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Weakly Supervised Dense Event Captioning in Videos |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| When do random forests fail? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Why Is My Classifier Discriminatory? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| With Friends Like These, Who Needs Adversaries? |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| cpSGD: Communication-efficient and differentially-private distributed SGD |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| e-SNLI: Natural Language Inference with Natural Language Explanations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |