▬▬ 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 🙂
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