7.4k citations · 24.2k across the 136 of their papers we have counts for
149 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…
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…
Versatile Energy-Based Probabilistic Models for High Energy Physics
Taoli Cheng, Aaron Courville
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have ac…
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…
Latent State Marginalization as a Low-cost Approach for Improving Exploration
Dinghuai Zhang, Aaron Courville, Yoshua Bengio +3
While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilisti…
Riemannian Diffusion Models
Chin-Wei Huang, Milad Aghajohari, Avishek Joey Bose +2
Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riema…