Generative flow networks (GFlowNets) are amortized variational inference...
We introduce BatchGFN – a novel approach for pool-based active learning ...
In the last decades, the capacity to generate large amounts of data in
s...
Generative Flow Networks (or GFlowNets for short) are a family of
probab...
Latent variable models (LVMs) with discrete compositional latents are an...
Tackling the most pressing problems for humanity, such as the climate cr...
Generative Flow Networks (GFlowNets) have demonstrated significant
perfo...
Bayesian Inference offers principled tools to tackle many critical probl...
In many applications of machine learning, like drug discovery and materi...
Generative flow networks (GFlowNets) are a family of algorithms for trai...
Generative Flow Networks (GFlowNets) are a method for learning a stochas...
This paper is about the problem of learning a stochastic policy for
gene...
Epistemic uncertainty is the part of out-of-sample prediction error due ...
Classical approaches for one-class problems such as one-class SVM (Schol...
IRGAN is an information retrieval (IR) modeling approach that uses a
the...