23 citations · 32 across the 11 of their papers we have counts for
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cs.LG2024
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…
cs.LG2024★ 3 cited
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim, Sanghyeok Choi, Hyeonah Kim +3
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method…
q-bio.BM2024★ 1 cited
Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
Hyeonah Kim, Minsu Kim, Sanghyeok Choi +1
The challenge of discovering new molecules with desired properties is crucial in domains like drug discovery and material design. Recent advances in deep learning-based generative…