Streaming Bayes GFlowNets
Authors: Tiago Silva, Daniel Augusto de Souza, Diego Mesquita
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our case studies in linear preference learning and phylogenetic inference showcase the effectiveness of SB-GFlow Nets in sampling from an unnormalized posterior in a streaming setting. As expected, we also observe that SB-GFlow Nets is significantly faster than repeatedly training a GFlow Net from scratch to sample from the full posterior. 5 Experiments We show that SB-GFlow Nets can accurately learn the posterior distribution conditioned on the streaming data for one toy and two practically relevant applications. |
| Researcher Affiliation | Academia | Tiago da Silva Getulio Vargas Foundation tiago.henrique@fgv.br Daniel Augusto de Souza University College London daniel.souza.21@ucl.ac.uk Diego Mesquita Getulio Vargas Foundation diego.mesquita@fgv.br |
| Pseudocode | Yes | Algorithm 1 Training a SB-GFlow Net by minimizing LSB |
| Open Source Code | Yes | Answer: [Yes] Justification: Provided in a zip file. |
| Open Datasets | No | We generate the data by simulating JC69 s model for a collection of N = 7 species and a substitution rate of λ = 5 10 1 (see [59], Chapter 1). At each iteration, we sample a new DNA subsequence of size 102 for each species and update SB-GFlow Net according to Algorithm 1. We assume that x [[0, 4]]d and d = 24 and that the data was simulated from the observational model. The paper uses simulated/generated data for its experiments and does not provide links or citations to pre-existing, publicly available datasets. |
| Dataset Splits | No | The paper describes a streaming data setting where data is continuously collected and processed. It does not explicitly mention traditional train/validation/test dataset splits with percentages or counts for its experiments. |
| Hardware Specification | Yes | Experiments were run in a cluster equipped with A100 and V100 GPUs, using a single GPU per run. |
| Software Dependencies | No | To approximately solve the optimization problem outlined in Algorithm 1, we employed the Adam optimizer [27] with a learning rate of 10 3 for the p F s parameters and 10 1 for log Zt, following recommendations from [37]. It mentions the optimizer but no specific software library versions (e.g., PyTorch, TensorFlow version). |
| Experiment Setup | Yes | We provide below details for reproducing our experiments for each considered generative task. To approximately solve the optimization problem outlined in Algorithm 1, we employed the Adam optimizer [27] with a learning rate of 10 3 for the p F s parameters and 10 1 for log Zt, following recommendations from [37]. Also, we linearly decreased the learning rate during training. |