Fetching the paper…
Reading the bibliography…
In this paper, we are concerned with rotation equivariance on 2D point cloud data.
Principles of Mathematical Analysis
Walter Rudin · 1953
Earlier work this paper cites.
A proof of Newton’s power sum formulas
JA Eidswick · 1968
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
A computer algorithm for reconstructing a scene from two projections
H. C. Longuet-Higgins · 1981
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
Backpropagation applied to handwritten zip code recognition
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel · 1989
Earlier work this paper cites.
Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman · 2003
Earlier work this paper cites.
Distinctive Image Features from Scale-Invariant Keypoints
David G. Lowe · 2004
Earlier work this paper cites.
Groups and Symmetries
Yvette Kosmann-Schwarzbach · 2010
Earlier work this paper cites.
Kinectfusion: Real-time 3d reconstruction and interaction using a moving depth camera
Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, and Andrew Fitzgibbon · 2011
Earlier work this paper cites.
Reconstructing the world* in six days
Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm · 2015
Earlier work this paper cites.
Spatial transformer networks
Max Jaderberg, Karen Simonyan, Andrew Zisserman, and koray kavukcuoglu · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhudinov, Rich Zemel, and Yoshua Bengio · 2015
Earlier work this paper cites.
Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
Earlier work this paper cites.
YFCC100M: The new data in multimedia research
Bart Thomee, David A. Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li · 2016
Earlier work this paper cites.
PointNet: Deep learning on point sets for 3D classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
Earlier work this paper cites.
Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Harmonic networks: Deep translation and rotation equivariance
Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, and Gabriel J. Brostow · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Cited alongside, same era.
Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
Cited alongside, same era.
Invariant and equivariant graph networks
Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman · 2018
Cited alongside, same era.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
Learning steerable filters for rotation equivariant CNNs
Maurice Weiler, Fred A. Hamprecht, and Martin Storath · 2018
Cited alongside, same era.
Homogeneous vector bundles and
Jimmy Aronsson · 2021
Closest in time.
Big Dipper 20210116.jpg, used under Creative Commons Attribution-ShareAlike 4.0 International
BreakdownDiode · 2021
Closest in time.
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Closest in time.
Equivariant Convolutional Networks (PhD Thesis)
Taco S. Cohen · 2021
Closest in time.
Vector neurons: A general framework for so(3)-equivariant networks
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas J. Guibas · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Attentional ShapeContextNet for Point Cloud Recognition
Saining Xie, Sainan Liu, Zeyu Chen, and Zhuowen Tu · 2018
Cited alongside, same era.
Learning to find good correspondences
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua · 2018
Cited alongside, same era.
PyTorch Lightning, Mar. 2019
William Falcon and The PyTorch Lightning team · 2019
Cited alongside, same era.
Universal invariant and equivariant graph neural networks
Nicolas Keriven and Gabriel Peyré · 2019
Cited alongside, same era.
Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
Cited alongside, same era.
On the universality of invariant networks
Haggai Maron, Ethan Fetaya, Nimrod Segol, and Yaron Lipman · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
Nadav Dym and Haggai Maron · 2021
Closest in time.
A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi, Max Welling, and Andrew Gordon Wilson · 2021
Closest in time.
A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups
Marc Finzi, Max Welling, and Andrew Gordon Gordon Wilson · 2021
Closest in time.
Geometric Deep Learning and Equivariant Neural Networks
Jan E. Gerken, Jimmy Aronsson, Oscar Carlsson, Hampus Linander, Fredrik Ohlsson, Christoffer Petersson, and Daniel Persson · 2021
Closest in time.
A wigner-eckart theorem for group equivariant convolution kernels
Leon Lang and Maurice Weiler · 2021
Closest in time.
Embed me if you can: A geometric perceptron
Pavlo Melnyk, Michael Felsberg, and Mårten Wadenbäck · 2021
Closest in time.
Scalars are universal: Equivariant machine learning, structured like classical physics
Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, and Ben Blum-Smith · 2021
Closest in time.
Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling · 2021
Closest in time.
Sgmnet: Learning rotation-invariant point cloud representations via sorted gram matrix
Jianyun Xu, Xin Tang, Yushi Zhu, Jie Sun, and Shiliang Pu · 2021
Closest in time.
A simple equivariant machine learning method for dynamics based on scalars
Weichi Yao, Kate Storey-Fisher, David W. Hogg, and Soledad Villar · 2021
Closest in time.
Universal approximations of invariant maps by neural networks
Dmitry Yarotsky · 2021
Closest in time.
Point transformer
Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip H.S. Torr, and Vladlen Koltun · 2021
Closest in time.
T-Net: Effective permutation-equivariant network for two-view correspondence learning
Zhen Zhong, Guobao Xiao, Linxin Zheng, Yan Lu, and Jiayi Ma · 2021
Closest in time.