| A Bayesian Approach to Argument-Based Reasoning for Attack Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Causal Framework for Discovering and Removing Direct and Indirect Discrimination |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Characterization Theorem for a Modal Description Logic |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Convolutional Approach for Misinformation Identification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Core-Guided Approach to Learning Optimal Causal Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Correlated Topic Model Using Word Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Data-Driven Approach to Infer Knowledge Base Representation for Natural Language Relations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Deep Neural Network for Chinese Zero Pronoun Resolution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Functional Dynamic Boltzmann Machine |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A General Multi-agent Epistemic Planner Based on Higher-order Belief Change |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| A General Notion of Equivalence for Abstract Argumentation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Goal Reasoning Agent for Controlling UAVs in Beyond-Visual-Range Air Combat |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Group-Based Personalized Model for Image Privacy Classification and Labeling |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| A Model for Accountable Ordinal Sorting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Monte Carlo Tree Search approach to Active Malware Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Neural Model for Joint Event Detection and Summarization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Novel Symbolic Approach to Verifying Epistemic Properties of Programs |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| A Partitioning Algorithm for Maximum Common Subgraph Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| A Random Model for Argumentation Framework: Phase Transitions, Empirical Hardness, and Heuristics |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Reasoning System for a First-Order Logic of Limited Belief |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Recursive Shortcut for CEGAR: Application To The Modal Logic K Satisfiability Problem |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Reduction based Method for Coloring Very Large Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| A Robust Noise Resistant Algorithm for POI Identification from Flickr Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Scalable Approach to Chasing Multiple Moving Targets with Multiple Agents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Sequence Labeling Convolutional Network and Its Application to Handwritten String Recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Structural Representation Learning for Multi-relational Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Study of Unrestricted Abstract Argumentation Frameworks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Unifying Framework for Probabilistic Belief Revision |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Variational Autoencoding Approach for Inducing Cross-lingual Word Embeddings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| AGRA: An Analysis-Generation-Ranking Framework for Automatic Abbreviation from Paper Titles |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| ATL Strategic Reasoning Meets Correlated Equilibrium |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| AccGenSVM: Selectively Transferring from Previous Hypotheses |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Accelerated Local Anomaly Detection via Resolving Attributed Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Acceptability Semantics for Weighted Argumentation Frameworks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Achieving Coordination in Multi-Agent Systems by Stable Local Conventions under Community Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Active Learning for Black-Box Semantic Role Labeling with Neural Factors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Elicitation of Preferences under Uncertainty in Sequential Decision Making Problems |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Adaptive Group Sparse Multi-task Learning via Trace Lasso |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Adaptive Hypergraph Learning for Unsupervised Feature Selection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Learning Rate via Covariance Matrix Based Preconditioning for Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Manifold Regularized Matrix Factorization for Data Clustering |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Adaptive Semantic Compositionality for Sentence Modelling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Adaptive Semi-Supervised Learning with Discriminative Least Squares Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptively Unified Semi-supervised Learning for Cross-Modal Retrieval |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Additive Merge-and-Shrink Heuristics for Diverse Action Costs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Affinity Learning for Mixed Data Clustering |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Agent Design Consistency Checking via Planning |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Aggregating Crowd Wisdoms with Label-aware Autoencoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
1 |
| Algorithmic Bias in Autonomous Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Abstraction-Refinement Methodology for Reasoning about Network Games |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| An Admissible HTN Planning Heuristic |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| An Algorithm for Constructing and Solving Imperfect Recall Abstractions of Large Extensive-Form Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Attention-based Regression Model for Grounding Textual Phrases in Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| An Effective Learnt Clause Minimization Approach for CDCL SAT Solvers |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| An Improved Approximation Algorithm for the Subpath Planning Problem and Its Generalization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| An Improved Decision-DNNF Compiler |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Analogy-preserving functions: A way to extend Boolean samples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Angle Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Answering Conjunctive Regular Path Queries over Guarded Existential Rules |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| App Download Forecasting: An Evolutionary Hierarchical Competition Approach |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Approximate Large-scale Multiple Kernel k-means Using Deep Neural Network |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Approximating Discrete Probability Distribution of Image Emotions by Multi-Modal Features Fusion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Attachment Centrality for Weighted Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Autoencoder Regularized Network For Driving Style Representation Learning |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Automatic Assessment of Absolute Sentence Complexity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Automatic Generation of Grounded Visual Questions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatic Synthesis of Smart Table Constraints by Abstraction of Table Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Autonomous Task Sequencing for Customized Curriculum Design in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Basket-Sensitive Personalized Item Recommendation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Bayesian Aggregation of Categorical Distributions with Applications in Crowdsourcing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Dynamic Mode Decomposition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Belief Change in a Preferential Non-monotonic Framework |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Belief Manipulation Through Propositional Announcements |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bernoulli Rank-1 Bandits for Click Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Forks: Finding and Ranking Star Factorings for Decoupled Search |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond the Nystrom Approximation: Speeding up Spectral Clustering using Uniform Sampling and Weighted Kernel k-means |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bilateral Multi-Perspective Matching for Natural Language Sentences |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Binary Linear Compression for Multi-label Classification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Blue Skies: A Methodology for Data-Driven Clear Sky Modelling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Boosted Zero-Shot Learning with Semantic Correlation Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bounded Timed Propositional Temporal Logic with Past Captures Timeline-based Planning with Bounded Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bounding the Inefficiency of Compromise |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bridging the Gap between Observation and Decision Making: Goal Recognition and Flexible Resource Allocation in Dynamic Network Interdiction |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Budget-Constrained Dynamics in Multiagent Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| CFNN: Correlation Filter Neural Network for Visual Object Tracking |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| COG-DICE: An Algorithm for Solving Continuous-Observation Dec-POMDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cake Cutting: Envy and Truth |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Cardinality Encodings for Graph Optimization Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Cascade Dynamics Modeling with Attention-based Recurrent Neural Network |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Category-aware Next Point-of-Interest Recommendation via Listwise Bayesian Personalized Ranking |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cause-Effect Knowledge Acquisition and Neural Association Model for Solving A Set of Winograd Schema Problems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Characterising the Manipulability of Boolean Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Classical Generalized Probabilistic Satisfiability |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Classification and Representation Joint Learning via Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Coarse-to-Fine Lifted MAP Inference in Computer Vision |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cognitive-Inspired Conversational-Strategy Reasoner for Socially-Aware Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Collaborative Rating Allocation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Combining DL-Lite_{bool}^N with Branching Time: A gentle Marriage |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Combining Models from Multiple Sources for RGB-D Scene Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Compact MDDs for Pseudo-Boolean Constraints with At-Most-One Relations in Resource-Constrained Scheduling Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Comparing Strategic Secrecy and Stackelberg Commitment in Security Games |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Compressed Nonparametric Language Modelling |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compromise-free Pathfinding on a Navigation Mesh |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Computing Bayes-Nash Equilibria in Combinatorial Auctions with Continuous Value and Action Spaces |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Computing an Approximately Optimal Agreeable Set of Items |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conditional Generative Adversarial Networks for Commonsense Machine Comprehension |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Conflict-driven ASP Solving with External Sources and Program Splits |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Confusion Graph: Detecting Confusion Communities in Large Scale Image Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Constrained Bayesian Reinforcement Learning via Approximate Linear Programming |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constraint Games revisited |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Constraint-Based Symmetry Detection in General Game Playing |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Contest Design with Uncertain Performance and Costly Participation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Context Attentive Bandits: Contextual Bandit with Restricted Context |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Context-Based Reasoning on Privacy in Internet of Things |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Context-aware Path Ranking for Knowledge Base Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ContextCare: Incorporating Contextual Information Networks to Representation Learning on Medical Forum Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Contextual Covariance Matrix Adaptation Evolutionary Strategies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Contract Design for Energy Demand Response |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence and Quality of Iterative Voting Under Non-Scoring Rules |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convolutional 2D LDA for Nonlinear Dimensionality Reduction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convolutional-Match Networks for Question Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Coordinated Versus Decentralized Exploration In Multi-Agent Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Core Stability in Hedonic Games among Friends and Enemies: Impact of Neutrals |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Correlational Dueling Bandits with Application to Clinical Treatment in Large Decision Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cost-Effective Active Learning from Diverse Labelers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Count-Based Exploration in Feature Space for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cross-Domain Recommendation: An Embedding and Mapping Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cross-Granularity Graph Inference for Semantic Video Object Segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cross-modal Common Representation Learning by Hybrid Transfer Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Crowd Learning: Improving Online Decision Making Using Crowdsourced Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DDoS Event Forecasting using Twitter Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DRLnet: Deep Difference Representation Learning Network and An Unsupervised Optimization Framework |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data-driven Random Fourier Features using Stein Effect |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deceptive Path-Planning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Decreasing Uncertainty in Planning with State Prediction |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Context: A Neural Language Model for Large-scale Networked Documents |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Deep Descriptor Transforming for Image Co-Localization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Forest: Towards An Alternative to Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Graphical Feature Learning for Face Sketch Synthesis |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Learning at Alibaba |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Matrix Factorization Models for Recommender Systems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Multi-species Embedding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Multiple Instance Hashing for Object-based Image Retrieval |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Neural Networks for High Dimension, Low Sample Size Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Ordinal Regression Based on Data Relationship for Small Datasets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Supervised Hashing with Nonlinear Projections |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Deep-dense Conditional Random Fields for Object Co-segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DeepAM: Migrate APIs with Multi-modal Sequence to Sequence Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| DeepFM: A Factorization-Machine based Neural Network for CTR Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DeepFacade: A Deep Learning Approach to Facade Parsing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DeepStory: Video Story QA by Deep Embedded Memory Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Defending Against Man-In-The-Middle Attack in Repeated Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Demystifying Neural Style Transfer |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deterministic, Strategyproof, and Fair Cake Cutting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Disambiguating Energy Disaggregation: A Collective Probabilistic Approach |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Discovering Relevance-Dependent Bicluster Structure from Relational Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Discriminant Tensor Dictionary Learning with Neighbor Uncorrelation for Image Set Based Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Discriminative Bayesian Nonparametric Clustering |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Discriminative Deep Hashing for Scalable Face Image Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Discriminative Dictionary Learning With Ranking Metric Embedded for Person Re-Identification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Disguise Adversarial Networks for Click-through Rate Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributed Accelerated Proximal Coordinate Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diverse Neuron Type Selection for Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Diverse Weighted Bipartite b-Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Diversifying Personalized Recommendation with User-session Context |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Don't Bury your Head in Warnings: A Game-Theoretic Approach for Intelligent Allocation of Cyber-security Alerts |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Doubly Sparsifying Network |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dual Inference for Machine Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Track Multimodal Automatic Learning through Human-Robot Interaction |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Dynamic Compositional Neural Networks over Tree Structure |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dynamic Logic for Data-aware Systems: Decidability Results |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Multi-Task Learning with Convolutional Neural Network |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Multi-View Hashing for Online Image Retrieval |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynamic Programming Bipartite Belief Propagation For Hyper Graph Matching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Earth Mover's Distance Pooling over Siamese LSTMs for Automatic Short Answer Grading |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Effective Deep Memory Networks for Distant Supervised Relation Extraction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Effective Representing of Information Network by Variational Autoencoder |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficiency Through Procrastination: Approximately Optimal Algorithm Configuration with Runtime Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Inference and Computation of Optimal Alternatives for Preference Languages Based On Lexicographic Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Computation of Extensions for Dynamic Abstract Argumentation Frameworks: An Incremental Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Inference for Untied MLNs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Kernel Selection via Spectral Analysis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Label Contamination Attacks Against Black-Box Learning Models |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Efficient Optimal Search under Expensive Edge Cost Computation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Private ERM for Smooth Objectives |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Weighted Model Integration via SMT-Based Predicate Abstraction |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient and Complete FD-solving for extended array constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient, Safe, and Probably Approximately Complete Learning of Action Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficiently Enforcing Path Consistency on Qualitative Constraint Networks by Use of Abstraction |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| EigenNet: Towards Fast and Structural Learning of Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Embedding-based Representation of Categorical Data by Hierarchical Value Coupling Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Encoding and Recall of Spatio-Temporal Episodic Memory in Real Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| End-to-End Prediction of Buffer Overruns from Raw Source Code via Neural Memory Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| End-to-end optimization of goal-driven and visually grounded dialogue systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enhancing Campaign Design in Crowdfunding: A Product Supply Optimization Perspective |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Enhancing Sustainability of Complex Epidemiological Models through a Generic Multilevel Agent-based Approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Entity Suggestion with Conceptual Expanation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Epistemic-entrenchment Characterization of Parikh’s Axiom |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Equi-Reward Utility Maximizing Design in Stochastic Environments |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equilibria in Ordinal Games: A Framework based on Possibility Theory. |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating the size of search trees by sampling with domain knowledge |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Exclusivity Regularized Machine: A New Ensemble SVM Classifier |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exemplar-centered Supervised Shallow Parametric Data Embedding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Explicit Knowledge-based Reasoning for Visual Question Answering |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploiting High-Order Information in Heterogeneous Multi-Task Feature Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Music Play Sequence for Music Recommendation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploration of Tree-based Hierarchical Softmax for Recurrent Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring Personalized Neural Conversational Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Extracting Visual Knowledge from the Web with Multimodal Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Factorized Asymptotic Bayesian Policy Search for POMDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair Allocation based on Diminishing Differences |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair Division of a Graph |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair and Efficient Social Choice in Dynamic Settings |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fashion Style Generator |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Change Point Detection on Dynamic Social Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Network Embedding Enhancement via High Order Proximity Approximation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Parallel Training of Neural Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fast Preprocessing for Robust Face Sketch Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Fast Recursive Low-rank Tensor Learning for Regression |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fast SVM Trained by Divide-and-Conquer Anchors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Sparse Gaussian Markov Random Fields Learning Based on Cholesky Factorization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Faster Conflict Generation for Dynamic Controllability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Feature Selection via Scaling Factor Integrated Multi-Class Support Vector Machines |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finding Prototypes of Answers for Improving Answer Sentence Selection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finding Robust Solutions to Stable Marriage |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Flexible Orthogonal Neighborhood Preserving Embedding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| FolkPopularityRank: Tag Recommendation for Enhancing Social Popularity using Text Tags in Content Sharing Services |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Forecast the Plausible Paths in Crowd Scenes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Foundations of Declarative Data Analysis Using Limit Datalog Programs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| From Automation to Autonomous Systems: A Legal Phenomenology with Problems of Accountability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| From Decimation to Local Search and Back: A New Approach to MaxSAT |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| From Ensemble Clustering to Multi-View Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| From Qualitative to Quantitative Dominance Pruning for Optimal Planning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Front-to-End Bidirectional Heuristic Search with Near-Optimal Node Expansions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Further Results on Predicting Cognitive Abilities for Adaptive Visualizations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| GDL-III: A Description Language for Epistemic General Game Playing |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Game Engine Learning from Video |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Target Assignment and Path Finding Using Answer Set Programming |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Generating Context-Free Grammars using Classical Planning |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Generating Hard Random Boolean Formulas and Disjunctive Logic Programs |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Global-residual and Local-boundary Refinement Networks for Rectifying Scene Parsing Predictions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Grounding of Human Environments and Activities for Autonomous Robots |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Group-wise Deep Co-saliency Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Handling Noise in Boolean Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Handling non-local dead-ends in Agent Planning Programs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hashtag Recommendation for Multimodal Microblog Using Co-Attention Network |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Heuristic Online Goal Recognition in Continuous Domains |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hierarchical Feature Selection with Recursive Regularization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical LSTM with Adjusted Temporal Attention for Video Captioning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Task Network Planning with Task Insertion and State Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| High Dimensional Bayesian Optimization using Dropout |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Unlabeled Web Videos Help Complex Event Detection? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| How to Form Winning Coalitions in Mixed Human-Computer Settings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How to Keep a Knowledge Base Synchronized with Its Encyclopedia Source |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Human-Centric Justification of Machine Learning Predictions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hybrid Neural Networks for Learning the Trend in Time Series |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Identifying Human Mobility via Trajectory Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Image Gradient-based Joint Direct Visual Odometry for Stereo Camera |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Image Matching via Loopy RNN |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Image-embodied Knowledge Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Importance-Aware Semantic Segmentation for Autonomous Driving System |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Improved Bounded Matrix Completion for Large-Scale Recommender Systems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved Deep Embedded Clustering with Local Structure Preservation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improved Neural Machine Translation with Source Syntax |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improved Strong Worst-case Upper Bounds for MDP Planning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Improving Classification Accuracy of Feedforward Neural Networks for Spiking Neuromorphic Chips |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Learning-from-Crowds through Expert Validation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Improving Reinforcement Learning with Confidence-Based Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Improving Stochastic Block Models by Incorporating Power-Law Degree Characteristic |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving the Generalization Performance of Multi-class SVM via Angular Regularization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Incomplete Attribute Learning with auxiliary labels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Incomplete Label Distribution Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incremental Decision Making Under Risk with the Weighted Expected Utility Model |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Incremental Matrix Factorization: A Linear Feature Transformation Perspective |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Induction of Interpretable Possibilistic Logic Theories from Relational Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Inferring Human Attention by Learning Latent Intentions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Inferring Implicit Event Locations from Context with Distributional Similarities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instability Prediction in Power Systems using Recurrent Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Instance-Level Label Propagation with Multi-Instance Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Integrating Specialized Classifiers Based on Continuous Time Markov Chain |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Intelligent Belief State Sampling for Conformant Planning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interaction-based ontology alignment repair with expansion and relaxation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Interactive Attention Networks for Aspect-Level Sentiment Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Interactive Image Segmentation via Pairwise Likelihood Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Interactive Narrative Personalization with Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inverse Covariance Estimation with Structured Groups |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverted Bilingual Topic Models for Lexicon Extraction from Non-parallel Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Is My Object in This Video? Reconstruction-based Object Search in Videos |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Iterative Entity Alignment via Joint Knowledge Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| JM-Net and Cluster-SVM for Aerial Scene Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Joint Capped Norms Minimization for Robust Matrix Recovery |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint Learning on Relevant User Attributes in Micro-blog |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Joint Training for Pivot-based Neural Machine Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Knowledge Graph Representation with Jointly Structural and Textual Encoding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Landmarks for Numeric Planning Problems |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Large-scale Online Kernel Learning with Random Feature Reparameterization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large-scale Subspace Clustering by Fast Regression Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lazy-Grounding for Answer Set Programs with External Source Access |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Co-Substructures by Kernel Dependence Maximization |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Learning Concise Representations of Users' Influences through Online Behaviors |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning Conversational Systems that Interleave Task and Non-Task Content |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Discriminative Correlation Subspace for Heterogeneous Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Discriminative Recommendation Systems with Side Information |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Feature Engineering for Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Hedonic Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Homophily Couplings from Non-IID Data for Joint Feature Selection and Noise-Resilient Outlier Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Latest Classifiers without Additional Labeled Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Multi-level Region Consistency with Dense Multi-label Networks for Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Sentence Representation with Guidance of Human Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Sparse Representations in Reinforcement Learning with Sparse Coding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning User Dependencies for Recommendation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning a Ground Truth Ranking Using Noisy Approval Votes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning deep structured network for weakly supervised change detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning from Demonstrations with High-Level Side Information |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning from Ontology Streams with Semantic Concept Drift |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning to Explain Entity Relationships by Pairwise Ranking with Convolutional Neural Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning to Hallucinate Face Images via Component Generation and Enhancement |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Identify Ambiguous and Misleading News Headlines |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Learn Programs from Examples: Going Beyond Program Structure |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
1 |
| Learning to Read Irregular Text with Attention Mechanisms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Run Heuristics in Tree Search |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning with Previously Unseen Features |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning-Based Abstractions for Nonlinear Constraint Solving |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lexical Sememe Prediction via Word Embeddings and Matrix Factorization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Life-Stage Modeling by Customer-Manifold Embedding |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Linear Manifold Regularization with Adaptive Graph for Semi-supervised Dimensionality Reduction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Link Prediction via Ranking Metric Dual-Level Attention Network Learning |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Link Prediction with Spatial and Temporal Consistency in Dynamic Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Locality Adaptive Discriminant Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Locality Constrained Deep Supervised Hashing for Image Retrieval |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Locality Preserving Matching |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Locality Preserving Projections for Grassmann manifold |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Locality in Random SAT Instances |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Locally Consistent Bayesian Network Scores for Multi-Relational Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Locally Linear Factorization Machines |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Logic Tensor Networks for Semantic Image Interpretation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Logic on MARS: Ontologies for Generalised Property Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Logistic Markov Decision Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Lossy Compression of Pattern Databases Using Acyclic Random Hypergraphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MAM-RNN: Multi-level Attention Model Based RNN for Video Captioning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| MAT: A Multimodal Attentive Translator for Image Captioning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| ME-MD: An Effective Framework for Neural Machine Translation with Multiple Encoders and Decoders |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Maintaining Communication in Multi-Robot Tree Coverage |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Making Cross Products and Guarded Ontology Languages Compatible |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Manipulating Gale-Shapley Algorithm: Preserving Stability and Remaining Inconspicuous |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Manipulating Opinion Diffusion in Social Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mapping Repair in Ontology-based Data Access Evolving Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Maximum Expected Likelihood Estimation for Zero-resource Neural Machine Translation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Measuring the Intensity of Attacks in Argumentation Graphs with Shapley Value |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mechanism Design for Strategic Project Scheduling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mechanisms for Online Organ Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mention Recommendation for Twitter with End-to-end Memory Network |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Microblog Sentiment Classification via Recurrent Random Walk Network Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mining Convex Polygon Patterns with Formal Concept Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Modal Consistency based Pre-Trained Multi-Model Reuse |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Model Checking Multi-Agent Systems against LDLK Specifications |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Modeling Hebb Learning Rule for Unsupervised Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modeling Physicians' Utterances to Explore Diagnostic Decision-making |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Modeling Trajectories with Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modelling the Working Week for Multi-Step Forecasting using Gaussian Process Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Most Probable Explanations for Probabilistic Database Queries |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multi-Agent Planning with Baseline Regret Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Multi-Class Support Vector Machine via Maximizing Multi-Class Margins |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Component Nonnegative Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Instance Learning with Key Instance Shift |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multi-Modal Word Synset Induction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-Positive and Unlabeled Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Stream Deep Similarity Learning Networks for Visual Tracking |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multi-Task Deep Reinforcement Learning for Continuous Action Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-instance multi-label active learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-view Feature Learning with Discriminative Regularization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multimodal Linear Discriminant Analysis via Structural Sparsity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multimodal Storytelling via Generative Adversarial Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiple Indefinite Kernel Learning for Feature Selection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiple Kernel Clustering Framework with Improved Kernels |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiple-Profile Prediction-of-Use Games |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multiple-Weight Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Multiwinner Rules on Paths From k-Borda to Chamberlin–Courant |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Name Nationality Classification with Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nash Equilibria in Concurrent Games with Lexicographic Preferences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near-Feasible Stable Matchings with Budget Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Networked Fairness in Cake Cutting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| New Metrics and Algorithms for Stochastic Goal Recognition Design Problems |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| No Pizza for You: Value-based Plan Selection in BDI Agents |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| No Time to Observe: Adaptive Influence Maximization with Partial Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-Determinism and the Dynamics of Knowledge |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonlinear Hybrid Planning with Deep Net Learned Transition Models and Mixed-Integer Linear Programming |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Nonlinear Maximum Margin Multi-View Learning with Adaptive Kernel |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Numeric Planning via Abstraction and Policy Guided Search |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Object Allocation via Swaps along a Social Network |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Object Detection Meets Knowledge Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Object Recognition with and without Objects |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Obtaining High-Quality Label by Distinguishing between Easy and Hard Items in Crowdsourcing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Omniscient Debugging for Cognitive Agent Programs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On Automating the Doctrine of Double Effect |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Coalitional Manipulation for Multiwinner Elections: Shortlisting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Computing World Views of Epistemic Logic Programs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Creating Complementary Pattern Databases |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| On Gleaning Knowledge from Multiple Domains for Active Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Neighborhood Singleton Consistencies |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Querying Incomplete Information in Databases under Bag Semantics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Subset Selection with General Cost Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Complexity of Enumerating the Extensions of Abstract Argumentation Frameworks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Complexity of Learning from Label Proportions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Computational Complexity of Gossip Protocols |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Kernelization of Global Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Power and Limitations of Deception in Multi-Robot Adversarial Patrolling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Bridged Pruning for Real-Time Search with Arbitrary Lookaheads |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Decision-Making for Scalable Autonomous Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Multitask Relative Similarity Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Optimization of Video-Ad Allocation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Reputation Fraud Campaign Detection in User Ratings |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Robust Low-Rank Tensor Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Roommate Allocation Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ontology-Mediated Query Answering for Key-Value Stores |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Ontology-Mediated Querying with the Description Logic EL: Trichotomy and Linear Datalog Rewritability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Open Category Classification by Adversarial Sample Generation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Operation Frames and Clubs in Kidney Exchange |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Opinion-aware Knowledge Graph for Political Ideology Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Optimal Escape Interdiction on Transportation Networks |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| Optimal Feature Selection for Decision Robustness in Bayesian Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimal Posted-Price Mechanism in Microtask Crowdsourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimizing Ratio of Monotone Set Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Order Statistics for Probabilistic Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ordinal Zero-Shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Orthogonal and Nonnegative Graph Reconstruction for Large Scale Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parameterised Verification of Data-aware Multi-Agent Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Pareto Optimal Allocation under Uncertain Preferences |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Parsing Natural Language Conversations using Contextual Cues |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Person Re-Identification by Deep Joint Learning of Multi-Loss Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Personnel Scheduling as Satisfiability Modulo Theories |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Pessimistic Leader-Follower Equilibria with Multiple Followers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Plan Explanations as Model Reconciliation: Moving Beyond Explanation as Soliloquy |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Plato's Cave in the Dempster-Shafer land--the Link between Pignistic and Plausibility Transformations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Player Movement Models for Video Game Level Generation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Playing Repeated Network Interdiction Games with Semi-Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Positive unlabeled learning via wrapper-based adaptive sampling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Posted Pricing sans Discrimination |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Predicting Alzheimer's Disease Cognitive Assessment via Robust Low-Rank Structured Sparse Model |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predicting Human Interaction via Relative Attention Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Predicting the Quality of Short Narratives from Social Media |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Privacy Issues Regarding the Application of DNNs to Activity-Recognition using Wearables and Its Countermeasures by Use of Adversarial Training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Privacy and Autonomous Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Privileged Matrix Factorization for Collaborative Filtering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Privileged Multi-label Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Proactive and Reactive Coordination of Non-dedicated Agent Teams Operating in Uncertain Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Probability Bounds for Overlapping Coalition Formation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Process Plan Controllers for Non-Deterministic Manufacturing Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Projection Free Rank-Drop Steps |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Projective Low-rank Subspace Clustering via Learning Deep Encoder |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Proportional Rankings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proposing a Highly Accurate Hybrid Component-Based Factorised Preference Model in Recommender Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pure Nash Equilibria in Online Fair Division |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Query Answering in Ontologies under Preference Rankings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Query Conservative Extensions in Horn Description Logics with Inverse Roles |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Query Rewriting for DL-Lite with n-ary Concrete Domains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Query-Driven Discovery of Anomalous Subgraphs in Attributed Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| RHash: Robust Hashing via L_infinity-norm Distortion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| ROUTE: Robust Outlier Estimation for Low Rank Matrix Recovery |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Radar: Residual Analysis for Anomaly Detection in Attributed Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random Shifting for CNN: a Solution to Reduce Information Loss in Down-Sampling Layers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Real-Time Navigation in Classical Platform Games via Skill Reuse |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Reasoning about Probabilities in Unbounded First-Order Dynamical Domains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Recognizing Top-Monotonic Preference Profiles in Polynomial Time |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reconstruction-based Unsupervised Feature Selection: An Embedded Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reduction Techniques for Model Checking and Learning in MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reformulating Queries: Theory and Practice |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| Regional Concept Drift Detection and Density Synchronized Drift Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement Learning with a Corrupted Reward Channel |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Relatedness-based Multi-Entity Summarization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Relaxed Exists-Step Plans in Planning as SMT |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Representativeness-aware Aspect Analysis for Brand Monitoring in Social Media |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Rescale-Invariant SVM for Binary Classification |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
3 |
| Responsible Autonomy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Restricted Chase (Non)Termination for Existential Rules with Disjunctions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Retaining Data from Streams of Social Platforms with Minimal Regret |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Revisiting Unrestricted Rebut and Preferences in Structured Argumentation. |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Advertisement Allocation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Asymmetric Bayesian Adaptive Matrix Factorization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Quadratic Programming for Price Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Robust Regression via Heuristic Hard Thresholding |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Softmax Regression for Multi-class Classification with Self-Paced Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust Survey Aggregation with Student-t Distribution and Sparse Representation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Role Forgetting for ALCOQH(universal role)-Ontologies Using an Ackermann-Based Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| SEVEN: Deep Semi-supervised Verification Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SPMC: Socially-Aware Personalized Markov Chains for Sparse Sequential Recommendation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SVD-Based Screening for the Graphical Lasso |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SVD-free Convex-Concave Approaches for Nuclear Norm Regularization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Safe Inductions: An Algebraic Study |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Saliency Guided End-to-End Learning for Weakly Supervised Object Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Salient Object Detection with Semantic Priors |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sample Efficient Policy Search for Optimal Stopping Domains |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sampling for Approximate Maximum Search in Factorized Tensor |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Constraint-based Virtual Data Center Allocation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Normalized Cut with Improved Spectral Rotation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scaling Active Search using Linear Similarity Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Score Aggregation via Spectral Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Search and Learn: On Dead-End Detectors, the Traps they Set, and Trap Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| See without looking: joint visualization of sensitive multi-site datasets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Segmenting Chinese Microtext: Joint Informal-Word Detection and Segmentation with Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Self-Paced Multitask Learning with Shared Knowledge |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-paced Compensatory Deep Boltzmann Machine for Semi-Structured Document Embedding |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Self-paced Convolutional Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-paced Mixture of Regressions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-weighted Multiview Clustering with Multiple Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semantic Visualization for Short Texts with Word Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semantics for Active Integrity Constraints Using Approximation Fixpoint Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Semi-Supervised Deep Hashing with a Bipartite Graph |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-Supervised Learning for Surface EMG-based Gesture Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-supervised Feature Selection via Rescaled Linear Regression |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-supervised Learning over Heterogeneous Information Networks by Ensemble of Meta-graph Guided Random Walks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-supervised Max-margin Topic Model with Manifold Posterior Regularization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Semi-supervised Orthogonal Graph Embedding with Recursive Projections |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sense Beauty by Label Distribution Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sequence Prediction with Unlabeled Data by Reward Function Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Should Robots be Obedient? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sifting Common Information from Many Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Single-Image 3D Scene Parsing Using Geometric Commonsense |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Single-Pass PCA of Large High-Dimensional Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SitNet: Discrete Similarity Transfer Network for Zero-shot Hashing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Smoothing Method for Approximate Extensive-Form Perfect Equilibrium |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Social Pressure in Opinion Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Socialized Word Embeddings |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Softpressure: A Schedule-Driven Backpressure Algorithm for Coping with Network Congestion |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Solving Integer Linear Programs with a Small Number of Global Variables and Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Solving Probability Problems in Natural Language |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Solving Stochastic Boolean Satisfiability under Random-Exist Quantification |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Stacked Similarity-Aware Autoencoders |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stacking With Auxiliary Features |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stance Classification with Target-specific Neural Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Staying Ahead of the Game: Adaptive Robust Optimization for Dynamic Allocation of Threat Screening Resources |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stochastic Constraint Programming with And-Or Branch-and-Bound |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
3 |
| Stochastic Online Anomaly Analysis for Streaming Time Series |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Storage Fit Learning with Unlabeled Data |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Strategically knowing how |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stratified Strategy Selection for Unit Control in Real-Time Strategy Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Multi-Context Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Strong Inconsistency in Nonmonotonic Reasoning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Strong Syntax Splitting for Iterated Belief Revision |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Student-t Process Regression with Student-t Likelihood |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Super-Human AI for Strategic Reasoning: Beating Top Pros in Heads-Up No-Limit Texas Hold'em |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Supervised Deep Features for Software Functional Clone Detection by Exploiting Lexical and Syntactical Information in Source Code |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Swift Logic for Big Data and Knowledge Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Symbolic LTLf Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Symbolic Priors for RNN-based Semantic Parsing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Symmetric Non-negative Latent Factor Models for Undirected Large Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Synchronisation Games on Hypergraphs |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Synthesizing Samples for Zero-shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tactics of Adversarial Attack on Deep Reinforcement Learning Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tag Disentangled Generative Adversarial Network for Object Image Re-rendering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tag-Aware Personalized Recommendation Using a Hybrid Deep Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Temporal Planning for Compilation of Quantum Approximate Optimization Circuits |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Temporal Planning with Clock-Based SMT Encodings |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
2 |
| Temporal Sequences of Qualitative Information: Reasoning about the Topology of Constant-Size Moving Regions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Temporalising Separation Logic for Planning with Search Control Knowledge |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Tensor Based Knowledge Transfer Across Skill Categories for Robot Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tensor Completion with Side Information: A Riemannian Manifold Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Tensor Decomposition with Missing Indices |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Bag Semantics of Ontology-Based Data Access |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Condorcet Principle for Multiwinner Elections: From Shortlisting to Proportionality |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The DNA Word Design Problem: A New Constraint Model and New Results |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| The Hard Problems Are Almost Everywhere For Random CNF-XOR Formulas |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Impact of Treewidth on ASP Grounding and Solving |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
2 |
| The Minds of Many: Opponent Modeling in a Stochastic Game |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Mixing of Markov Chains on Linear Extensions in Practice |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Off-Switch Game |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Tractability of the Shapley Value over Bounded Treewidth Matching Games |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
3 |
| Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Thresholding Bandits with Augmented UCB |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thwarting Vote Buying Through Decoy Ballots |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Top-k Supervise Feature Selection via ADMM for Integer Programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Tosca: Operationalizing Commitments Over Information Protocols |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Understanding the Invertibility of Convolutional Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Training Group Orthogonal Neural Networks with Privileged Information |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TransNet: Translation-Based Network Representation Learning for Social Relation Extraction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transfer Learning in Multi-Armed Bandits: A Causal Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Two dimensional Large Margin Nearest Neighbor for Matrix Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding How Feature Structure Transfers in Transfer Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding People Lifestyles: Construction of Urban Movement Knowledge Graph from GPS Trajectory |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding and Exploiting Language Diversity |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unified Representation and Lifted Sampling for Generative Models of Social Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Reinforcement Learning Algorithms: Survey and Experiments |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Deep Video Hashing with Balanced Rotation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unsupervised Learning of Deep Feature Representation for Clustering Egocentric Actions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| User Profile Preserving Social Network Embedding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Mixtures of Gaussian Processes for Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Verification of Broadcasting Multi-Agent Systems against an Epistemic Strategy Logic |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Verifying Fault-tolerance in Parameterised Multi-Agent Systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Vertex-Weighted Hypergraph Learning for Multi-View Object Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Video Question Answering via Hierarchical Spatio-Temporal Attention Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Voting by sequential elimination with few voters |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| WALKING WALKing walking: Action Recognition from Action Echoes |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Weakening Covert Networks by Minimizing Inverse Geodesic Length |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Weighted Double Q-learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Weighted Model Integration with Orthogonal Transformations |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| What Can You Do with a Rock? Affordance Extraction via Word Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| What to Do Next: Modeling User Behaviors by Time-LSTM |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| When Does Label Propagation Fail? A View from a Network Generative Model |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| When Security Games Hit Traffic: Optimal Traffic Enforcement Under One Sided Uncertainty |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| When Will Negotiation Agents Be Able to Represent Us? The Challenges and Opportunities for Autonomous Negotiators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Who to Invite Next? Predicting Invitees of Social Groups |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Why Can't You Convince Me? Modeling Weaknesses in Unpersuasive Arguments |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Why You Should Charge Your Friends for Borrowing Your Stuff |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| XOR-Sampling for Network Design with Correlated Stochastic Events |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |