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..
A theory of continuous generative flow networks
Authors: Salem Lahlou, Tristan Deleu, Pablo Lemos, Dinghuai Zhang, Alexandra Volokhova, Alex Hernรกndez-Garcฤฑฬa, Lena Nehale Ezzine, Yoshua Bengio, Nikolay Malkin
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments |
| Researcher Affiliation | Academia | 1Mila 2Universit e de Montr eal 3Ciela Institute 4CIFAR. |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code for these experiments can be found at https://github. com/saleml/continuous-gfn. |
| Open Datasets | Yes | We evaluate GFlow Nets and baselines on two synthetic densities: a 2-dimensional mixture of 9 Gaussians and the 10-dimensional funnel from MCMC literature (Hoffman & Gelman, 2011)." and "We train a GFlow Net as described above on the Image Net-32 dataset (treated as a set of terminating states) |
| Dataset Splits | No | The paper discusses training and testing but does not explicitly provide details about validation dataset splits (percentages, counts, or methodology). |
| Hardware Specification | Yes | This is much shorter than other state-of-the-art work (such as Lipman et al. (2022)) and takes less than 3 days on a single V100 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We learned the concentration parameters of the Beta distributions, which were restrained to the interval [0.1, 5.1], using a three-layered neural network with 128 units per layer, and leaky Re LU activation for ๐ ๐ 0...We trained the models for 20,000 iterations. For both the DB and TB losses, we used a learning rate of 10 3 for the parameters of ๐๐น, ๐๐ต, ๐๐น(๐ 0, ) (and log ๐for TB, ๐ขfor DB). The learning rate was annealed using a discount factor of 0.5 every 2500 iterations. |