Local Search GFlowNets
Authors: Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. We present our experimental results on 6 biochemical tasks, including molecule optimization and biological sequence design. |
| Researcher Affiliation | Collaboration | Minsu Kim & Taeyoung Yun KAIST Emmanuel Bengio Recursion Dinghuai Zhang Mila, Universit e de Montr eal Yoshua Bengio Mila, Universit e de Montr eal, CIFAR Sungsoo Ahn POSTECH Jinkyoo Park KAIST, Omelet |
| Pseudocode | Yes | Algorithm 1 Local Search GFlow Net (LS-GFN) |
| Open Source Code | Yes | Source code is available: https://github.com/dbsxodud-11/ls_gfn. |
| Open Datasets | Yes | QM9. Our goal is to generate a small molecule graph...obtained via a pre-trained MXMNet (Zhang et al., 2020) proxy. s EH. Our goal is to generate binders of the s EH protein...provided by the pre-trained proxy model provided by (Bengio et al., 2021). TFBind8. Our goal is to generate a string of length 8 of nucleotides...Trabucco et al., 2022). RNA-Binding...introduced by Sinai et al. (2020). |
| Dataset Splits | No | The paper mentions 'training dataset D' and 'training rounds' but does not specify explicit train/validation/test splits with percentages, absolute counts, or well-defined standard split references. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like 'ADAM' and 'MLP architecture', and notes following implementations from 'Shen et al. (2023)', but does not provide specific version numbers for any key software components (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | For all tasks, we use ADAM (Kingma & Ba, 2015) optimizer with learning rate 1e-2 for log Zθ, 1e-4 for forward and backward policy. We use different reward exponent β...For LS-GFN, we have set the number of candidate samples as M = 4 and the local search interaction to I = 7 as default values. Table 3, which specifies 'Number of Layers', 'Hidden Units', 'Reward Exponent (β)', and 'Training Rounds (T)'. |