2 citations · 2 across the 4 of their papers we have counts for
4 papers
Learning Layer-wise Equivariances Automatically using Gradients
Tycho F. A. van der Ouderaa, Alexander Immer, Mark van der Wilk
Convolutions encode equivariance symmetries into neural networks leading to better generalisation performance. However, symmetries provide fixed hard constraints on the functions a…
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion
Alexandru Meterez, Amir Joudaki, Francesco Orabona +3
Normalization layers are one of the key building blocks for deep neural networks. Several theoretical studies have shown that batch normalization improves the signal propagation, b…
Hodge-Aware Contrastive Learning
Alexander Möllers, Alexander Immer, Vincent Fortuin +1
Simplicial complexes prove effective in modeling data with multiway dependencies, such as data defined along the edges of networks or within other higher-order structures. Their sp…
Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk +2
Selecting hyperparameters in deep learning greatly impacts its effectiveness but requires manual effort and expertise. Recent works show that Bayesian model selection with Laplace…