152 citations · 465 across the 17 of their papers we have counts for
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Two-Level K-FAC Preconditioning for Deep Learning
Nikolaos Tselepidis, Jonas Kohler, Antonio Orvieto
In the context of deep learning, many optimization methods use gradient covariance information in order to accelerate the convergence of Stochastic Gradient Descent. In particular,…
Training Invertible Linear Layers through Rank-One Perturbations
Andreas Krämer, Jonas Köhler, Frank Noé
Many types of neural network layers rely on matrix properties such as invertibility or orthogonality. Retaining such properties during optimization with gradient-based stochastic o…
Equivariant Flows: Exact Likelihood Generative Learning for Symmetric Densities
Jonas Köhler, Leon Klein, Frank Noé
Normalizing flows are exact-likelihood generative neural networks which approximately transform samples from a simple prior distribution to samples of the probability distribution…
Stochastic Normalizing Flows
Hao Wu, Jonas Köhler, Frank Noé
The sampling of probability distributions specified up to a normalization constant is an important problem in both machine learning and statistical mechanics. While classical stoch…