Portrait de Dinghuai Zhang

Dinghuai Zhang

Doctorat - UdeM
Superviseur⋅e principal⋅e
Aaron Courville
Co-supervisor
Yoshua Bengio
Sujets de recherche
Modèles génératifs
Modèles probabilistes
Site web

Publications

Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization
Dinghuai Zhang
Ricky T. Q. Chen
Cheng-Hao Liu
Aaron Courville
Yoshua Bengio
We tackle the problem of sampling from intractable high-dimensional density functions, a fundamental task that often appears in machine lear… (voir plus)ning and statistics. We extend recent sampling-based approaches that leverage controlled stochastic processes to model approximate samples from these target densities. The main drawback of these approaches is that the training objective requires full trajectories to compute, resulting in sluggish credit assignment issues due to use of entire trajectories and a learning signal present only at the terminal time. In this work, we present Diffusion Generative Flow Samplers (DGFS), a sampling-based framework where the learning process can be tractably broken down into short partial trajectory segments, via parameterizing an additional"flow function". Our method takes inspiration from the theory developed for generative flow networks (GFlowNets), allowing us to make use of intermediate learning signals. Through various challenging experiments, we demonstrate that DGFS achieves more accurate estimates of the normalization constant than closely-related prior methods.
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
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
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
Let the Flows Tell: Solving Graph Combinatorial Problems with GFlowNets
Dinghuai Zhang
Hanjun Dai
Nikolay Malkin
Aaron Courville
Yoshua Bengio
Ling Pan
openreview.net
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan
Nikolay Malkin
Dinghuai Zhang
Yoshua Bengio
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an en… (voir plus)ergy function), reinforcement learning (as they learn a policy to sample composed objects through a sequence of steps), generative models (as they learn to represent and sample from a distribution) and amortized variational methods (as they can be used to learn to approximate and sample from an otherwise intractable posterior, given a prior and a likelihood). They are trained to generate an object
2023-07-03
Proceedings of the 40th International Conference on Machine Learning (publié)
doi.org
openreview.net