Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …

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 …

Significant scientific discoveries have driven the progress of human civilisation. The
explosion of scientific literature and data has created information barriers across disciplines …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

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 …

Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a
distribution over discrete objects proportional to their rewards. GFlowNets exhibit a …

GFlowNets are probabilistic models that learn a stochastic policy that sequentially generates
compositional structures, such as molecular graphs. They are trained with the objective of …

Advances in single-cell omics allow for unprecedented insights into the transcription profiles
of individual cells. When combined with large-scale perturbation screens, through which …

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

We consider the problem of sampling from a discrete and structured distribution as a
sequential decision problem, where the objective is to find a stochastic policy such that …