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..
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Authors: Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron C. Courville, Yoshua Bengio, Ling Pan
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on a variety of different CO tasks with synthetic and realistic data, we demonstrate that GFlow Net policies can efficiently find high-quality solutions. |
| Researcher Affiliation | Collaboration | Dinghuai Zhang Mila Hanjun Dai Google Deep Mind Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan Mila |
| Pseudocode | Yes | Algorithm 1 GFlow Net training algorithm |
| Open Source Code | Yes | Our implementation is open-sourced at https://github.com/zdh Narsil/GFlow Net-Comb Opt. |
| Open Datasets | Yes | For MIS problems, we follow the setup in the MIS benchmark from Böther et al. (2022)... we take the more complicated RB graphs (Xu & Li, 2000) following Karalias & Loukas (2020). For realistic data, we take the SATLIB dataset (Hoos et al., 2000)... For the other two tasks... we adopt BA graphs (Barabási & Albert, 1999) following Sun et al. (2022). |
| Dataset Splits | Yes | For all datasets, we use a validation set and a test set, both with 500 data points. ... We choose the hyperparameters of each algorithm based on the validation performance. |
| Hardware Specification | Yes | Regarding hardware, we use NVIDIA Tesla V100 Volta GPUs. |
| Software Dependencies | No | The paper mentions "pygurobi python package" and "cvxpy python package" but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | For the GFlow Net architecture, we use graph isomorphism network (Xu et al., 2019, GIN) with 5 hidden layers and 256 dimensional hidden size... We use the Adam optimizer with default 1 10 3 learning rate without hyperparameter tuning. ... with batchsize equals 64... The training keeps 20 epochs... we use an inverse temperature of 500 for all the tasks. |