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Erik Bekkers
Приєднався 6 вер 2013
UvA Deep Learning 2 (2023): Group Equivariant Deep Learning - Lecture 1
UvA Deep Learning 2 (2023): Group Equivariant Deep Learning - Lecture 1
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Відео
2.1 Expectation Variance (UvA - Machine Learning 1 - 2020)
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See uvaml1.github.io for annotated slides and a week-by-week overview of the course. This work is licensed under a Creative Commons Attribution 4.0 International License (creativecommons.org/licenses/by/4.0/).
Group Equivariant Deep Learning - Lecture 3.7: Gauge equivariant graph NNs
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.6: Literature survey (Regular equivariant graph NNs)
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.5: Literature survey (3D Steerable graph NNs)
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.4: Group Theory (SO(3) irreps, Wigner-D, Clebsch-Gordan)
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.3: Tensor products as conditional linear layers
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.2: Equivariant message passing as non-linear convolution
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 3.1: Motivation for SE(3) equivariant graph NNs
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Lecture 3: Equivariant graph neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 2.7: Derivation of Harmonic Networks from Regular G-Convs
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Lecture 2: Steerable group convolutional neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 2.6: Activation Functions for Steerable G-CNNs
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Lecture 2: Steerable group convolutional neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 2.5: Steerable group convolutions
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Lecture 2: Steerable group convolutional neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 2.4: Group Theory (Induced representation, feature fields)
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Lecture 2: Steerable group convolutional neural networks Part of the module on Group Equivariant Deep Learning of the Deep Learning 2 Course (uvadl2c.github.io) at the University of Amsterdam
Group Equivariant Deep Learning - Lecture 2.3: Group Theory (Irreducible representations, Fourier)
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Group Equivariant Deep Learning - Lecture 2.3: Group Theory (Irreducible representations, Fourier)
Group Equivariant Deep Learning - Lecture 2.2: Revisiting Regular G-Convs with Steerable Kernels
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Group Equivariant Deep Learning - Lecture 2.2: Revisiting Regular G-Convs with Steerable Kernels
Group Equivariant Deep Learning - Lecture 2.1: Steerable kernels/basis functions
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Group Equivariant Deep Learning - Lecture 2.1: Steerable kernels/basis functions
Group Equivariant Deep Learning - Lecture 1.1: Introduction
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Group Equivariant Deep Learning - Lecture 1.1: Introduction
Group Equivariant Deep Learning - Lecture 1.6: Group theory (Homogeneous/quotient spaces)
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Group Equivariant Deep Learning - Lecture 1.6: Group theory (Homogeneous/quotient spaces)
Group Equivariant Deep Learning - Lecture 1.5: A Brief History of G-CNNs
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Group Equivariant Deep Learning - Lecture 1.5: A Brief History of G-CNNs
Group Equivariant Deep Learning - Lecture 1.7: Group convolutions are all you need
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Group Equivariant Deep Learning - Lecture 1.7: Group convolutions are all you need
Group Equivariant Deep Learning - Lecture 1.3: Regular group convolutional neural networks
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Group Equivariant Deep Learning - Lecture 1.3: Regular group convolutional neural networks
Group Equivariant Deep Learning - Lecture 1.4: Example
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Group Equivariant Deep Learning - Lecture 1.4: Example
Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)
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Group Equivariant Deep Learning - Lecture 1.2: Group theory (product, inverse, representations)
13.4 Decision Trees And Random Forests (UvA - Machine Learning 1 - 2020)
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13.4 Decision Trees And Random Forests (UvA - Machine Learning 1 - 2020)
13.3 Boosting (UvA - Machine Learning 1 - 2020)
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13.3 Boosting (UvA - Machine Learning 1 - 2020)
13.2 Bootstrapping And Feature Bagging (UvA - Machine Learning 1 - 2020)
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13.2 Bootstrapping And Feature Bagging (UvA - Machine Learning 1 - 2020)
13.1 Model Combination Methods Vs Bayesian Model Averaging (UvA - Machine Learning 1 - 2020)
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13.1 Model Combination Methods Vs Bayesian Model Averaging (UvA - Machine Learning 1 - 2020)
12.5 Gaussian Processes: Regression (UvA - Machine Learning 1 - 2020)
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12.5 Gaussian Processes: Regression (UvA - Machine Learning 1 - 2020)
12.4 Gaussian Processes With An Exponential Kernel (UvA - Machine Learning 1 - 2020)
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12.4 Gaussian Processes With An Exponential Kernel (UvA - Machine Learning 1 - 2020)
12.3 Gaussian Processes (UvA - Machine Learning 1 - 2020)
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12.3 Gaussian Processes (UvA - Machine Learning 1 - 2020)