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Group Equivariant Deep Learning - Lecture 1.3: Regular group convolutional neural networks
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Group Equivariant Deep Learning - 2022
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- 1 Group Equivariant Deep Learning - Lecture 1.1: Introduction
- 2 Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)
- 3 Group Equivariant Deep Learning - Lecture 1.3: Regular group convolutional neural networks
- 4 Group Equivariant Deep Learning - Lecture 1.4: Example
- 5 Group Equivariant Deep Learning - Lecture 1.5: A Brief History of G-CNNs
- 6 Group Equivariant Deep Learning - Lecture 1.6: Group theory (Homogeneous/quotient spaces)
- 7 Group Equivariant Deep Learning - Lecture 1.7: Group convolutions are all you need
- 8 Group Equivariant Deep Learning - Lecture 2.1: Steerable kernels/basis functions
- 9 Group Equivariant Deep Learning - Lecture 2.2: Revisiting Regular G-Convs with Steerable Kernels
- 10 Group Equivariant Deep Learning - Lecture 2.3: Group Theory (Irreducible representations, Fourier)
- 11 Group Equivariant Deep Learning - Lecture 2.4: Group Theory (Induced representation, feature fields)
- 12 Group Equivariant Deep Learning - Lecture 2.5: Steerable group convolutions
- 13 Group Equivariant Deep Learning - Lecture 2.6: Activation Functions for Steerable G-CNNs
- 14 Group Equivariant Deep Learning - Lecture 2.7: Derivation of Harmonic Networks from Regular G-Convs
- 15 Group Equivariant Deep Learning - Lecture 3.1: Motivation for SE(3) equivariant graph NNs
- 16 Group Equivariant Deep Learning - Lecture 3.2: Equivariant message passing as non-linear convolution
- 17 Group Equivariant Deep Learning - Lecture 3.3: Tensor products as conditional linear layers
- 18 Group Equivariant Deep Learning - Lecture 3.4: Group Theory (SO(3) irreps, Wigner-D, Clebsch-Gordan)
- 19 Group Equivariant Deep Learning - Lecture 3.5: Literature survey (3D Steerable graph NNs)
- 20 Group Equivariant Deep Learning - Lecture 3.6: Literature survey (Regular equivariant graph NNs)
- 21 Group Equivariant Deep Learning - Lecture 3.7: Gauge equivariant graph NNs
