Generative Flow Networks (GFlowNets) have been introduced as a method to sample a
diverse set of candidates in an active learning context, with a training objective that makes …

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 …

Abstract 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 …

High training costs of generative models and the need to fine-tune them for specific tasks
have created a strong interest in model reuse and composition. A key challenge in …

The recently proposed generative flow networks (GFlowNets) are a method of training a
policy to sample compositional discrete objects with probabilities proportional to a given …

We tackle the problem of sampling from intractable high-dimensional density functions, a
fundamental task that often appears in machine learning and statistics. We extend recent …

GFlowNets are probabilistic models that sequentially generate compositional structures
through a stochastic policy. Among GFlowNets, temperature-conditional GFlowNets can …

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role
in predicting properties of a molecule. In this paper we propose to use GFlowNets for …

Generative Flow Networks (GFlowNets) are amortized samplers that learn stochastic
policies to sequentially generate compositional objects from a given unnormalized reward …

Diffusion models excel at capturing complex data distributions, such as those of natural
images and proteins. While diffusion models are trained to represent the distribution in the …