Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

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 …

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 …

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

We study the problem of training diffusion models to sample from a distribution with a given
unnormalized density or energy function. We benchmark several diffusion-structured …

Generative flow networks (GFlowNets) are a family of algorithms for training a sequential
sampler of discrete objects under an unnormalized target density and have been …

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 …

Latent variable models (LVMs) with discrete compositional latents are an important but
challenging setting due to a combinatorially large number of possible configurations of the …