Undirected Graphical Models as Approximate Posteriors
Authors: Arash Vahdat, Evgeny Andriyash, William Macready
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results are provided in Sec. 4 on VAEs (Sec. 4.1), importance-weighted VAEs (Sec. 4.2), and structured prediction (Sec. 4.3), where we observe consistent improvement using UGMs. |
| Researcher Affiliation | Industry | 1NVIDIA, USA 2Sanctuary AI, Canada, Work done at D-Wave Systems. Correspondence to: Arash Vahdat <avahdat@nvidia.com>. |
| Pseudocode | Yes | Algorithm 1 summarizes training of DVAE##. |
| Open Source Code | Yes | Our implementation is available here. |
| Open Datasets | Yes | binarized MNIST (Salakhutdinov & Murray, 2008) and OMNIGLOT (Lake et al., 2015) datasets. |
| Dataset Splits | No | No explicit train/validation/test dataset splits were provided. While a 'test set' is mentioned, no details on validation or the partitioning of the full dataset are given. |
| Hardware Specification | No | No specific hardware details (such as CPU/GPU models, memory, or specific cloud instance types) were mentioned for running experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers were provided. The paper mentions 'Tensorflow' and 'Qu PA library' but without version details. |
| Experiment Setup | Yes | p(x|z) is represented using a fully-connected neural network having two 200-unit hidden layers, tanh activations, and batch normalization similar to DVAE++ (Vahdat et al., 2018b), DVAE# (Vahdat et al., 2018a), and Gum Bolt (Khoshaman & Amin, 2018). Training DVAE## is done using Algorithm 1 with s = 10 and t = 1 using a piece-wise linear relaxation (Andriyash et al., 2018) for relaxed Gibbs samples. We follow (Vahdat et al., 2018a) for batch size, learning rate schedule, and KL warm up parameters. |