Equivariant Neural Networks | Part 1/3 - Introduction

DeepFindr · Beginner ·📐 ML Fundamentals ·3y ago
▬▬ Papers / Resources ▬▬▬ Fabian Fuchs Equivariance: https://fabianfuchsml.github.io/equivariance1of2/ Deep Learning for Molecules: https://dmol.pub/dl/Equivariant.html Naturally Occuring Equivariance: https://distill.pub/2020/circuits/equivariance/ 3Blue1Brown Group Theory: https://www.youtube.com/watch?v=mH0oCDa74tE&t=552s&ab_channel=3Blue1Brown Group Equivariant CNNs: https://arxiv.org/abs/1602.07576 Equivariance vs Data Augmentation: https://arxiv.org/pdf/2202.03990.pdf ▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬ Music from #Uppbeat (free for Creators!): https://uppbeat.io/t/yokonap/birds License code: WXVHOOZRRWDUCKIU ▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬ All Icons are from flaticon: https://www.flaticon.com/authors/freepik ▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬ 00:00 Introduction 00:45 Equivariance and Invariance 03:03 CNNs are translation equivariant 04:00 Math notation 04:25 Visual intuition 05:08 Symmetries 06:22 Why are CNNs not rotation equivariant? 07:14 Inductive biases reduce the flexibility 08:10 What's wrong with data augmentations? 09:32 Motivations for Equivariant Neural Networks 09:55 You've unlocked a checkpoint. 10:07 Naturally occuring equivariance 10:50 Group Equivariant Convolutional Neural Networks 11:37 Group Theory (on a high level) 12:41 An example and the matrix notation 13:50 Group axioms 14:32 Cayley tables 15:33 Examples for groups 16:38 Applications of Equivariant Neural Networks 18:30 Final Checkpoint :) ▬▬ Support me if you like 🌟 ►Link to this channel: https://bit.ly/3zEqL1W ►Support me on Patreon: https://bit.ly/2Wed242 ►Buy me a coffee on Ko-Fi: https://bit.ly/3kJYEdl ►E-Mail: deepfindr@gmail.com ▬▬ My equipment 💻 - Microphone: https://amzn.to/3DVqB8H - Microphone mount: https://amzn.to/3BWUcOJ - Monitors: https://amzn.to/3G2Jjgr - Monitor mount: https://amzn.to/3AWGIAY - Height-adjustable table: https://amzn.to/3aUysXC - Ergonomic chair: https://amzn.to/3phQg7r - PC case: https://amzn.to/3jdlI2Y - GPU: https://amzn.to/3AWyzwy - Keyboard: https://amzn.to/2XskWHP
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1 Understanding Graph Neural Networks | Part 1/3 - Introduction
Understanding Graph Neural Networks | Part 1/3 - Introduction
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 2 Understanding Graph Neural Networks | Part 2/3 - GNNs and it's Variants
Understanding Graph Neural Networks | Part 2/3 - GNNs and it's Variants
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Understanding Convolutional Neural Networks | Part 2 / 3 - Wonders of the world CNN with PyTorch
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Understanding Convolutional Neural Networks | Part 3 / 3 - Transfer Learning and Explainable AI
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 8 How to use edge features in Graph Neural Networks (and PyTorch Geometric)
How to use edge features in Graph Neural Networks (and PyTorch Geometric)
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Explainable AI explained! | #3 LIME
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Explainable AI explained! | #4 SHAP
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 13 Explainable AI explained! | #5 Counterfactual explanations and adversarial attacks
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 35 How to handle Uncertainty in Deep Learning #2.1
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 36 How to handle Uncertainty in Deep Learning #2.2
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 ▶ Equivariant Neural Networks | Part 1/3 - Introduction
Equivariant Neural Networks | Part 1/3 - Introduction
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 54 Multidimensional Scaling (MDS) | Dimensionality Reduction Techniques (3/5)
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Chapters (20)

Introduction
0:45 Equivariance and Invariance
3:03 CNNs are translation equivariant
4:00 Math notation
4:25 Visual intuition
5:08 Symmetries
6:22 Why are CNNs not rotation equivariant?
7:14 Inductive biases reduce the flexibility
8:10 What's wrong with data augmentations?
9:32 Motivations for Equivariant Neural Networks
9:55 You've unlocked a checkpoint.
10:07 Naturally occuring equivariance
10:50 Group Equivariant Convolutional Neural Networks
11:37 Group Theory (on a high level)
12:41 An example and the matrix notation
13:50 Group axioms
14:32 Cayley tables
15:33 Examples for groups
16:38 Applications of Equivariant Neural Networks
18:30 Final Checkpoint :)
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