Portrait de Minsu Kim

Minsu Kim

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Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org
Local Search GFlowNets
Minsu Kim
Taeyoung YUN
Emmanuel Bengio
Dinghuai Zhang
Yoshua Bengio
Sungsoo Ahn
Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their re… (voir plus)wards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
2023-10-04
ArXiv (prépublication)
doi.org
arxiv.org