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..
GFlowNet-EM for Learning Compositional Latent Variable Models
Authors: Edward J Hu, Nikolay Malkin, Moksh Jain, Katie E Everett, Alexandros Graikos, Yoshua Bengio
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach, GFlow Net-EM, enables the training of expressive LVMs with discrete compositional latents, as shown by experiments on non-contextfree grammar induction and on images using discrete variational autoencoders (VAEs) without conditional independence enforced in the encoder. |
| Researcher Affiliation | Collaboration | 1Mila, Universit e de Montr eal 2Google Research 3Massachusetts Institute of Technology 4Stony Brook University 5CIFAR Fellow. |
| Pseudocode | Yes | Algorithm 1 GFlow Net-EM: Basic form with thresholding [...] Algorithm 2 GFlow Net-EM: E-step (sleep phase) |
| Open Source Code | Yes | Code: github.com/GFNOrg/GFlow Net-EM. |
| Open Datasets | Yes | We use a subset of Penn Tree Bank (PTB; Marcus et al., 1999) that contains sentences with 20 or fewer tokens. [...] We perform our experiments on the static MNIST dataset (Deng, 2012). |
| Dataset Splits | Yes | We use a subset of Penn Tree Bank (PTB; Marcus et al., 1999) that contains sentences with 20 or fewer tokens. Otherwise, we follow the preprocessing done by Kim et al. (2019). [...] We perform our experiments on the static MNIST dataset (Deng, 2012), with a 4 × 4 spatial latent representation and using dictionaries of sizes K ∈ {4, 8, 10} and dimensionality D = 1. |
| Hardware Specification | Yes | Grammar induction Our experiments with the context-free grammar take 23 hours to run to completion on a single V100 GPU [...] an E-step (training the GFlow Net encoder) takes approximately 25s for 400 updates, whereas the M-step (training the convolutional decoder) requires 10s for 400 updates on one A5000 GPU. |
| Software Dependencies | Yes | We use Torch-Struct (Rush, 2020) to perform marginalization and exact sampling in PCFGs. |
| Experiment Setup | Yes | Training hyperparameters are listed in Table 3. [...] We used a similar architecture as the one described in (van den Oord et al., 2017), adding batch normalization and additional downsizing and upsizing convolutional layers to obtain the smaller 4 × 4 latent representation. [...] For K= {4, 8} we trained the VQ-VAE model for 50 epochs with a learning rate of 2 × 10−4, reduced to 5 × 10−5 at epoch 25. [...] We disabled all batch normalization layers for the GFlow Net experiments and used a batch size of 128 in all our tests. |