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 …

In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by
a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate …

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 …

Discovering new promising molecule candidates that could translate into effective drugs is a
key scientific pursuit. However, factors such as the vastness and discreteness of the …

Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …

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 …

This paper presents a novel approach to multiobjective algorithms aimed at modeling the
Pareto set using neural networks. Whereas previous methods mainly focused on identifying …

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 …

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 …