Generative flow networks (GFlowNets) are amortized variational inference algorithms that
are trained to sample from unnormalized target distributions over compositional objects. A …

This paper builds bridges between two families of probabilistic algorithms:(hierarchical)
variational inference (VI), which is typically used to model distributions over continuous …

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 …

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 …

Abstract 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 …

Generative flow networks (GFlowNets) are a method for learning a stochastic policy for
generating compositional objects, such as graphs or strings, from a given unnormalized …

There are many frameworks for deep generative modeling, each often presented with their
own specific training algorithms and inference methods. Here, we demonstrate the …

We present energy-based generative flow networks (EB-GFN), a novel probabilistic
modeling algorithm for high-dimensional discrete data. Building upon the theory of …

Flow-based generative models parameterize probability distributions through an invertible
transformation and can be trained by maximum likelihood. Invertible residual networks …