Local Group Invariant Representations via Orbit Embeddings

Local Group Invariant Representations via Orbit Embeddings

6 December 2016

Bernhard Schölkopf

Papers citing "Local Group Invariant Representations via Orbit Embeddings"

11 / 11 papers shown

Title
Regularising for invariance to data augmentation improves supervised learning Aleksander Botev Matthias Bauer Soham De 37 14 0 07 Mar 2022
VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds Monami Banerjee Rudrasis Chakraborty Jose J. Bouza B. Vemuri 36 11 0 05 Jun 2021
On the Benefits of Invariance in Neural Networks Clare Lyle Mark van der Wilk Marta Z. Kwiatkowska Y. Gal Benjamin Bloem-Reddy OOD BDL 33 91 0 01 May 2020
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space Yingyi Ma Vignesh Ganapathiraman Yaoliang Yu Xinhua Zhang 21 1 0 25 Apr 2020
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 36 83 0 16 Aug 2018
Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network Risi Kondor Zhen Lin Shubhendu Trivedi 56 266 0 24 Jun 2018
A Kernel Theory of Modern Data Augmentation Tri Dao Albert Gu Alexander J. Ratner Virginia Smith Christopher De Sa Christopher Ré 27 190 0 16 Mar 2018
Spherical CNNs Taco S. Cohen Mario Geiger Jonas Köhler Max Welling 80 894 0 30 Jan 2018
Max-Margin Invariant Features from Transformed Unlabeled Data Dipan K. Pal Ashwin A. Kannan Gautam Arakalgud Marios Savvides 16 8 0 24 Oct 2017
Structured adaptive and random spinners for fast machine learning computations Mariusz Bojarski A. Choromańska K. Choromanski Francois Fagan Cédric Gouy-Pailler Anne Morvan Nourhan Sakr Tamás Sarlós Jamal Atif 35 35 0 19 Oct 2016
Learning from Conditional Distributions via Dual Embeddings Bo Dai Niao He Yunpeng Pan Byron Boots Le Song 35 21 0 15 Jul 2016