June 11, 2024, 4:46 a.m. | George Ma, Emmanuel Bengio, Yoshua Bengio, Dinghuai Zhang

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.05426v1 Announce Type: new
Abstract: GFlowNets have exhibited promising performance in generating diverse candidates with high rewards. These networks generate objects incrementally and aim to learn a policy that assigns probability of sampling objects in proportion to rewards. However, the current training pipelines of GFlowNets do not consider the presence of isomorphic actions, which are actions resulting in symmetric or isomorphic states. This lack of symmetry increases the amount of samples required for training GFlowNets and can result in inefficient …

abstract aim arxiv cs.lg current diverse generate however learn networks objects performance pipelines policy probability sampling symmetry training type

Senior Data Engineer

@ Displate | Warsaw

Principal Software Engineer

@ Microsoft | Prague, Prague, Czech Republic

Sr. Global Reg. Affairs Manager

@ BASF | Research Triangle Park, NC, US, 27709-3528

Senior Robot Software Developer

@ OTTO Motors by Rockwell Automation | Kitchener, Ontario, Canada

Coop - Technical Service Hub Intern

@ Teradyne | Santiago de Queretaro, MX

Coop - Technical - Service Inside Sales Intern

@ Teradyne | Santiago de Queretaro, MX