9 papers
GFlowNet Foundations
Yoshua Bengio, Salem Lahlou, Tristan Deleu +3
Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them…
Torsional-GFN: a conditional conformation generator for small molecules
Alexandra Volokhova, Léna Néhale Ezzine, Piotr GaiÅski +5
Generating stable molecular conformations is crucial in several drug discovery applications, such as estimating the binding affinity of a molecule to a target. Recently, generative…
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim, Minsu Kim, Taeyoung Yun +4
Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although reinforcement learning methods use proxy mo…
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Mohit Pandey, Gopeshh Subbaraj, Artem Cherkasov +2
Generative Flow Networks (GFlowNets) have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from rewards treated as…
Random Policy Evaluation Uncovers Policies of Generative Flow Networks
Haoran He, Emmanuel Bengio, Qingpeng Cai +1
The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sample objects proportionally to an unnormali…
Investigating Generalization Behaviours of Generative Flow Networks
Lazar Atanackovic, Emmanuel Bengio
Generative Flow Networks (GFlowNets, GFNs) are a generative framework for learning unnormalized probability mass functions over discrete spaces. Since their inception, GFlowNets ha…