Prerequisites I: Groups, Representations & Equivariance - Maurice Weiler

Maurice Weiler - Equivariant and Coordinate Independent Convolutional NetworksSee more

Maurice Weiler - Equivariant and Coordinate Independent Convolutional Networks

Prerequisites III: Manifolds & Fiber Bundles - Maurice WeilerSee more

Prerequisites III: Manifolds & Fiber Bundles - Maurice Weiler

Group Equivariant Deep Learning - Lecture 3.4: Group Theory (SO(3) irreps, Wigner-D, Clebsch-Gordan)See more

Group Equivariant Deep Learning - Lecture 3.4: Group Theory (SO(3) irreps, Wigner-D, Clebsch-Gordan)

Lecture 5: Equivariant CNNs II (Riemannian manifolds) - Maurice WeilerSee more

Lecture 5: Equivariant CNNs II (Riemannian manifolds) - Maurice Weiler

Prerequisites I: Groups, Representations & Equivariance - Maurice WeilerSee more

Prerequisites I: Groups, Representations & Equivariance - Maurice Weiler

A Program to Build E(N)-Equivariant Steerable CNNsSee more

A Program to Build E(N)-Equivariant Steerable CNNs

Lecture 4: Equivariant CNNs I (Euclidean Spaces) - Maurice WeilerSee more

Lecture 4: Equivariant CNNs I (Euclidean Spaces) - Maurice Weiler

Unsupervised Learning of Group Invariant and Equivariant RepresentationsSee more

Unsupervised Learning of Group Invariant and Equivariant Representations

Prerequisites II: Topology - Cristian BodnarSee more

Prerequisites II: Topology - Cristian Bodnar

05 Imperial's Deep learning course: Equivariance and InvarianceSee more

05 Imperial's Deep learning course: Equivariance and Invariance

Equivariant Neural Networks | Part 1/3 - IntroductionSee more

Equivariant Neural Networks | Part 1/3 - Introduction

Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)See more

Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)

Group Equivariant Deep Learning - Lecture 1.1: IntroductionSee more

Group Equivariant Deep Learning - Lecture 1.1: Introduction

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