82 citations · 129 across the 6 of their papers we have counts for
6 papers · 1 filter
Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds
Maurice Weiler, Patrick Forré, Erik Verlinde +1
Motivated by the vast success of deep convolutional networks, there is a great interest in generalizing convolutions to non-Euclidean manifolds. A major complication in comparison…
An Information-theoretic Approach to Distribution Shifts
Marco Federici, Ryota Tomioka, Patrick Forré
Safely deploying machine learning models to the real world is often a challenging process. Models trained with data obtained from a specific geographic location tend to fail when q…
Self Normalizing Flows
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini +3
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most propo…
FlipOut: Uncovering Redundant Weights via Sign Flipping
Andrei Apostol, Maarten Stol, Patrick Forré
Modern neural networks, although achieving state-of-the-art results on many tasks, tend to have a large number of parameters, which increases training time and resource usage. This…
Pruning via Iterative Ranking of Sensitivity Statistics
Stijn Verdenius, Maarten Stol, Patrick Forré
With the introduction of SNIP [arXiv:1810.02340v2], it has been demonstrated that modern neural networks can effectively be pruned before training. Yet, its sensitivity criterion h…
Learning Robust Representations via Multi-View Information Bottleneck
Marco Federici, Anjan Dutta, Patrick Forré +2
The information bottleneck principle provides an information-theoretic method for representation learning, by training an encoder to retain all information which is relevant for pr…