Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Undirected Graphical Models as Approximate Posteriors
Authors: Arash Vahdat, Evgeny Andriyash, William Macready
ICML 2020 | Venue PDF | 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 <EMAIL>. |
| 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 'Tensor๏ฌow' 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. |