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Found 7 papers, 5 papers with code
Towards equilibrium molecular conformation generation with GFlowNets
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
Crystal-GFN: sampling crystals with desirable properties and constraints
Accelerating material discovery holds the potential to greatly help mitigate the climate crisis.
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.
Generative Flow Networks for Discrete Probabilistic Modeling
We present energy-based generative flow networks (EB-GFN), a novel probabilistic modeling algorithm for high-dimensional discrete data.
Stochasticity in Neural ODEs: An Empirical Study
Stochastic regularization of neural networks (e. g. dropout) is a wide-spread technique in deep learning that allows for better generalization.
Semi-Conditional Normalizing Flows for Semi-Supervised Learning
This paper proposes a semi-conditional normalizing flow model for semi-supervised learning.
Cherenkov Detectors Fast Simulation Using Neural Networks
We propose a way to simulate Cherenkov detector response using a generative adversarial neural network to bypass low-level details.
