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].
Generative Flow Networks for Discrete Probabilistic Modeling
Authors: Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, Yoshua Bengio
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate EB-GFN s effectiveness on various probabilistic modeling tasks. Code is publicly available at github.com/zdh Narsil/EB GFN. and We test the algorithm on a variety of synthetic and real tasks, achieving competitive results ( 4). |
| Researcher Affiliation | Academia | 1Mila Quebec AI Institute and Universit e de Montr eal, Montreal, Quebec, Canada. |
| Pseudocode | Yes | Algorithm 1 EB-GFN joint training framework and Algorithm 2 GFlow Net-guided energy function update |
| Open Source Code | Yes | Code is publicly available at github.com/zdh Narsil/EB GFN. |
| Open Datasets | Yes | Omniglot, Silhouettes, Static MNIST, Dynamic MNIST are mentioned in Table 3. These are standard, well-known public datasets used in machine learning. |
| Dataset Splits | Yes | The validation and evaluation protocol is also kept aligned with Grathwohl et al. (2021b), where the checkpoint with the best negative log-likelihood on the validation set is reported. and The validation of EBM likelihood is achieved with 300000 step annealed importance sampling (AIS). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'ELU activation' but does not provide specific version numbers for any software dependencies, libraries, or programming languages. |
| Experiment Setup | Yes | For simplicity, we set K = D for the negative sampling step and use a training policy with α = 1 (no backward paths from training examples). Details can be found in C.1. and We use a 4 layer MLP with 256 hidden dimension and ELU activation (Clevert et al., 2016) as the energy function. The training of this energy function lasts 105 steps. An Adam optimizer with 1 10 3 learning rate is used to update the EBM. The batch size is 128. and For GFlow Net training, we use a mixed training policy with α = 0.5 and a schedule with linearly increasing K for the back-and-forth proposal. See C.2 for details. |