Group Equivariant Deep Learning - Lecture 1.7: Group convolutions are all you need

UvA Deep Learning 2 (2023): Group Equivariant Deep Learning - Lecture 1

GeoMedIA 2022

2.1 Expectation Variance (UvA - Machine Learning 1 - 2020)

Group Equivariant Deep Learning - Lecture 3.7: Gauge equivariant graph NNs

Group Equivariant Deep Learning - Lecture 3.6: Literature survey (Regular equivariant graph NNs)

Group Equivariant Deep Learning - Lecture 3.5: Literature survey (3D Steerable graph NNs)

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

Group Equivariant Deep Learning - Lecture 3.3: Tensor products as conditional linear layers

Group Equivariant Deep Learning - Lecture 3.2: Equivariant message passing as non-linear convolution

Group Equivariant Deep Learning - Lecture 3.1: Motivation for SE(3) equivariant graph NNs

Group Equivariant Deep Learning - Lecture 2.7: Derivation of Harmonic Networks from Regular G-Convs

Group Equivariant Deep Learning - Lecture 2.6: Activation Functions for Steerable G-CNNs

Group Equivariant Deep Learning - Lecture 2.4: Group Theory (Induced representation, feature fields)

Group Equivariant Deep Learning - Lecture 2.5: Steerable group convolutions

Group Equivariant Deep Learning - Lecture 2.3: Group Theory (Irreducible representations, Fourier)

Group Equivariant Deep Learning - Lecture 2.1: Steerable kernels/basis functions

Group Equivariant Deep Learning - Lecture 2.2: Revisiting Regular G-Convs with Steerable Kernels

Group Equivariant Deep Learning - Lecture 1.3: Regular group convolutional neural networks

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

Group Equivariant Deep Learning - Lecture 1.1: Introduction

Group Equivariant Deep Learning - Lecture 1.4: Example

Group Equivariant Deep Learning - Lecture 1.6: Group theory (Homogeneous/quotient spaces)

Group Equivariant Deep Learning - Lecture 1.7: Group convolutions are all you need

Group Equivariant Deep Learning - Lecture 1.5: A Brief History of G-CNNs