Generalized Variational Inference via Optimal Transport
Authors: Jinjin Chi, Zhichao Zhang, Zhiyao Yang, Jihong Ouyang, Hongbin Pei
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide the consistency analysis of approximate posteriors and demonstrate the practical effectiveness on Bayesian neural networks and variational autoencoders. |
| Researcher Affiliation | Academia | Jinjin Chi1,2, Zhichao Zhang1,2, Zhiyao Yang1,2, Jihong Ouyang1,2, Hongbin Pei3* 1 College of Computer Science and Technology, Jilin University, China 2 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China 3 MOE KLINNS Lab, School of Cyber Science and Engineering, Xi an Jiaotong University, China |
| Pseudocode | Yes | Algorithm 1: Computation of OT distance" and "Algorithm 2: Optimization of VOT |
| Open Source Code | No | The paper mentions that 'well-known variational inference methods with different α-divergences ... are implemented using publicly available code' and provides a link (https://github.com/Yingzhen Li/VRbound) for these baselines, and also mentions 'WVI(Ambrogioni et al. 2018), which is implemented upon the publicly available code 5' with link (https://github.com/zqkhan/wvi pytorch). However, it does not provide any statement or link for the source code of their proposed method (VOT). |
| Open Datasets | Yes | The linear regression task is performed on seven widely-used benchmark data sets from the UCI dataset repository 3. The statistics of the data sets are shown in Table 1. ... 3http://archive.ics.uci.edu/ml/datasets.html" and "Variational Autoencoder ... MNIST dataset4, a collection of handwritten digits from zero to nine. ... 4http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | Yes | Each data set is randomly split into 90% for training and 10% for testing. ... The sizes of training and testing are 60000 and 10000, respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions 'Adam optimizer' and 'automatic differentiation tools (Team 2015)' but does not provide specific version numbers for software libraries or dependencies used in their implementation. |
| Experiment Setup | Yes | In all methods, Adam optimizer is employed to adjust the learning rate with parameters β1=0.9, β2=0.999 and α=0.001 (Kingma and Ba 2015). The sample number S is set to 128 and the training epoch is set to 500. The entropy regularization parameter ε is set to 0.1. The constants a and b in λ are set to 2 10 3 and 2 104, respectively. |