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 [1].
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. |