Feb. 12, 2024, 5:42 a.m. | Hyeonah Kim Minsu Kim Sanghyeok Choi Jinkyoo Park

cs.LG updates on arXiv.org arxiv.org

This paper proposes a novel variant of GFlowNet, genetic-guided GFlowNet (Genetic GFN), which integrates an iterative genetic search into GFlowNet. Genetic search effectively guides the GFlowNet to high-rewarded regions, addressing global over-exploration that results in training inefficiency and exploring limited regions. In addition, training strategies, such as rank-based replay training and unsupervised maximum likelihood pre-training, are further introduced to improve the sample efficiency of Genetic GFN. The proposed method shows a state-of-the-art score of 16.213, significantly outperforming the reported best …

benchmark cs.lg cs.ne exploration global guides iterative novel optimization paper practical q-bio.bm search strategies training unsupervised

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Data Scientist AI / ML - Associate 2 -Bangalore

@ PwC | Bengaluru (SDC) - Bagmane Tech Park

Staff ML Engineer - Machine Learning

@ Visa | Bengaluru, India

Senior Data Scientist

@ IQVIA | Dublin, Ireland

Data Analyst ETL Expert

@ Bosch Group | Bengaluru, India