16 citations · 62 across the 13 of their papers we have counts for
13 papers
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
Dinghuai Zhang, Hanjun Dai, Esmeralda S. Whitammer +3
Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact algorithms, making them a tempting domain to apply machine learning methods. The highly s…
Stochastic Generative Flow Networks
Ling Pan, Dinghuai Zhang, Moksh Jain +2
Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures through the lens of "inference as contr…
Distributional GFlowNets with Quantile Flows
Dinghuai Zhang, Ling Pan, Ricky T. Q. Chen +2
Generative Flow Networks (GFlowNets) are a new family of probabilistic samplers where an agent learns a stochastic policy for generating complex combinatorial structure through a s…
Better Training of GFlowNets with Local Credit and Incomplete Trajectories
Ling Pan, Nikolay Malkin, Dinghuai Zhang +1
Generative Flow Networks or GFlowNets are related to Monte-Carlo Markov chain methods (as they sample from a distribution specified by an energy function), reinforcement learning (…
Generative Augmented Flow Networks
Ling Pan, Dinghuai Zhang, Aaron Courville +2
The Generative Flow Network is a probabilistic framework where an agent learns a stochastic policy for object generation, such that the probability of generating an object is propo…
Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning
Pihe Hu, Ling Pan, Yu Chen +2
Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing. Yet, it poses a critical…