Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

Group Equivariant Deep Learning - Lecture 3.7 Gauge equivariant graph NNs

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

Group Equivariant Deep Learning - Lecture 2.5 Steerable group convolutions

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

Group Equivariant Deep Learning - Lecture 2.1 Steerable kernelsbasis functions

Erik Bekkers

Group Equivariant Deep Learning - Lecture 1.1 Introduction

Erik Bekkers

Group Equivariant Deep Learning - Lecture 1.6 Group theory (Homogeneousquotient spaces)

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

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

Erik Bekkers

Group Equivariant Deep Learning - Lecture 1.4 Example

Erik Bekkers

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

Erik Bekkers

13.4 Decision Trees And Random Forests (UvA - Machine Learning 1 - 2020)

Erik Bekkers

13.3 Boosting (UvA - Machine Learning 1 - 2020)

Erik Bekkers

13.2 Bootstrapping And Feature Bagging (UvA - Machine Learning 1 - 2020)

Erik Bekkers

13.1 Model Combination Methods Vs Bayesian Model Averaging (UvA - Machine Learning 1 - 2020)

Erik Bekkers

12.5 Gaussian Processes Regression (UvA - Machine Learning 1 - 2020)

Erik Bekkers

12.4 Gaussian Processes With An Exponential Kernel (UvA - Machine Learning 1 - 2020)

Erik Bekkers

12.3 Gaussian Processes (UvA - Machine Learning 1 - 2020)

Erik Bekkers