Workshop on Equivariance and Data Augmentation
Generalization Effects of Linear Transformations in Data Augmentation - Hongyang R. Zhang
35:05
Workshop on Equivariance and Data Augmentation
Meta-Learning Symmetries - Chelsea Finn
32:28
Workshop on Equivariance and Data Augmentation
Amplifying Datasets: A Theoretical Perspective - Greg Valiant
35:21
Workshop on Equivariance and Data Augmentation
Leveraging permutation group symmetries for designing equivariant neural networks - Haggai Maron
34:12
Workshop on Equivariance and Data Augmentation
Unintended features of Euclidean symmetry equivariant neural networks - Tess E Smidt
29:47
Workshop on Equivariance and Data Augmentation
Neurally plausible mechanisms for learning selective and invariant representations - Fabio Anselmi
29:27
Workshop on Equivariance and Data Augmentation
Workshop on Equivariance and Data Augmentation - Panel - mod by Edgar Dobriban & Kostas Daniilidis
33:42
Workshop on Equivariance and Data Augmentation
A group-theoretic framework for data augmentation - Jane H Lee,
22:13
Workshop on Equivariance and Data Augmentation
Spin-Weighted Spherical CNNs - Carlos Esteves
15:21
Workshop on Equivariance and Data Augmentation
Learning with few labeled data - Pratik Chaudhari
23:17
Workshop on Equivariance and Data Augmentation
Algebraic Neural Networks: Symmetry and Stability - Alejandro Ribeiro
24:22
Workshop on Equivariance and Data Augmentation
Model-based Robust Deep Learning - Alexander Robey
26:03
Workshop on Equivariance and Data Augmentation
Learning Invariances through Backprop with Bayesian Model Selection - Mark van der Wilk
35:59
Workshop on Equivariance and Data Augmentation
Learning with Marginalized Augmenation - Kilian Q. Weinberger
34:30
Workshop on Equivariance and Data Augmentation
Equivariant Networks and Natural Graph Networks - Taco Cohen
36:23
Workshop on Equivariance and Data Augmentation
Generative Models and Symmetries - Danilo J. Rezende
30:01