Fetching the paper…
Reading the bibliography…
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits.
“Equivariant Hamiltonian Flows”, 2019
Danilo Rezende, Sébastien Racanière, Irina Higgins and Peter Toth · 1909
Earlier work this paper cites.
“Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex”
D.. Hubel and T.. Wiesel · 1962
Earlier work this paper cites.
“Dynamics of pattern formation in lateral-inhibition type neural fields”
Shun-ichi Amari · 1977
Earlier work this paper cites.
“Center-Surround Organization of Auditory Receptive Fields in the Owl”
Eric/ Knudsen and Masakazu Konishi · 1978
Earlier work this paper cites.
“Simplified neuron model as a principal component analyzer”
Erkki Oja · 1982
Earlier work this paper cites.
“The two-dimensional spectral structure of simple receptive fields in cat striate cortex.”
J.. Jones, A Stepnoski and Larry. Palmer · 1987
Earlier work this paper cites.
“Neural networks and principal component analysis: Learning from examples without local minima”
Pierre Baldi and Kurt Hornik · 1989
Earlier work this paper cites.
“Handwritten digit recognition with a back-propagation network”
Y. LeCun et al · 1990
Earlier work this paper cites.
“Eigenvalues of covariance matrices: Application to neural-network learning”
Yann Le, Ido Kanter and Sara Solla · 1991
Earlier work this paper cites.
“Optimal storage of invariant sets of patterns in neural network memories”
W Tarkowski, M Komarnicki and M Lewenstein · 1991
Earlier work this paper cites.
“Generalization in a linear perceptron in the presence of noise”
Anders Krogh and John Hertz · 1992
Earlier work this paper cites.
“Generalization in a large committee machine”
Henry Schwarze and John Hertz · 1992
Earlier work this paper cites.
“Properties of neural networks storing spatially correlated patterns”
R Monasson · 1992
Earlier work this paper cites.
“Storage of spatially correlated patterns in autoassociative memories”
Rémi Monasson · 1993
Earlier work this paper cites.
“Storage of sets of correlated data in neural network memories”
W Tarkowski and M Lewenstein · 1993
Earlier work this paper cites.
“Learning from correlated examples in a perceptron”
W Tarkowski and M Lewenstein · 1993
Earlier work this paper cites.
“Exact Solution for On-Line Learning in Multilayer Neural Networks”
David Saad and Sara. Solla · 1995
Earlier work this paper cites.
“Learning by on-line gradient descent”
M Biehl and H Schwarze · 1995
Earlier work this paper cites.
“Exact Solution for On-Line Learning in Multilayer Neural Networks”
David Saad and Sara. Solla · 1995
Earlier work this paper cites.
“On-line backpropagation in two-layered neural networks”
P Riegler and M Biehl · 1995
Earlier work this paper cites.
“Emergence of simple-cell receptive field properties by learning a sparse code for natural images”
Bruno Olshausen and David Field · 1996
Earlier work this paper cites.
“A coupled attractor model of the rodent head direction system”
A Redish, Adam Elga and David Touretzky · 1996
Earlier work this paper cites.
“Edges are the ’Independent Components’of Natural Scenes.”
Anthony Bell and Terrence Sejnowski · 1996
Earlier work this paper cites.
“Algorithm 778: L-BFGS-B: Fortran Subroutines for Large-Scale Bound-Constrained Optimization”
Ciyou Zhu, Richard. Byrd, Peihuang Lu and Jorge Nocedal · 1997
Earlier work this paper cites.
“Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey”
James. DiCarlo, Kenneth. Johnson and Steven. Hsiao · 1998
Earlier work this paper cites.
“A three–step algorithm for CANDECOMP/PARAFAC analysis of large data sets with multicollinearity”
Henk.. Kiers · 1998
Earlier work this paper cites.
“Hierarchical models of object recognition in cortex”
Maximilian Riesenhuber and Tomaso Poggio · 1999
Earlier work this paper cites.
“Synaptic Mechanisms and Network Dynamics Underlying Spatial Working Memory in a Cortical Network Model”
Albert Compte, Nicolas Brunel, Patricia. Goldman-Rakic and Xiao-Jing Wang · 2000
Earlier work this paper cites.
“Independent component analysis: algorithms and applications”
Aapo Hyvärinen and Erkki Oja · 2000
Earlier work this paper cites.
“Statistical mechanics of learning”
Andreas Engel and Christian Van · 2001
Earlier work this paper cites.
“A spherical Hopfield model”
D Bollé, Th Nieuwenhuizen, Iérez Castillo and T Verbeiren · 2003
Earlier work this paper cites.
“Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices”
Jinho Baik, Gérard Arous and Sandrine Péché · 2005
Earlier work this paper cites.
“Statistical models for neural encoding, decoding, and optimal stimulus design”
Liam Paninski, Jonathan Pillow and Jeremy Lewi · 2007
Earlier work this paper cites.
“Tensor Decompositions and Applications”
Tamara. Kolda and Brett. Bader · 2009
Earlier work this paper cites.
“Evaluation of pooling operations in convolutional architectures for object recognition”
Dominik Scherer, Andreas Müller and Sven Behnke · 2010
Earlier work this paper cites.
“Localization transition in symmetric random matrices”
F.. Metz, I. Neri and D. Bollé · 2010
Earlier work this paper cites.
“Efficient coding of natural images with a population of noisy Linear-Nonlinear neurons”
Yan Karklin and Eero Simoncelli · 2011
Cited alongside, same era.
“How Does the Brain Solve Visual Object Recognition?”
James. DiCarlo, Davide Zoccolan and Nicole. Rust · 2012
Cited alongside, same era.
“Modeling the impact of common noise inputs on the network activity of retinal ganglion cells”
Michael Vidne et al · 2012
Cited alongside, same era.
“Imagenet classification with deep convolutional neural networks”
A. Krizhevsky, I. Sutskever and G.E. Hinton · 2012
Cited alongside, same era.
“Group invariant scattering”
Stéphane Mallat · 2012
Cited alongside, same era.
“Efficient Coding of Spatial Information in the Primate Retina”
Eizaburo Doi et al · 2012
Cited alongside, same era.
“A mathematical theory of semantic development in deep neural networks”
A.M. Saxe, J.L. McClelland and S. Ganguli · 2019
Later among the works it cites.
“Kernel Random Matrices of Large Concentrated Data: the Example of GAN-Generated Images”
M.E.A. Seddik, M. Tamaazousti and R. Couillet · 2019
Later among the works it cites.
“A mathematical theory of semantic development in deep neural networks”
Andrew. Saxe, James. McClelland and Surya Ganguli · 2019
Later among the works it cites.
“On the spectral bias of neural networks”
N. Rahaman et al · 2019
Later among the works it cites.
“SGD on Neural Networks Learns Functions of Increasing Complexity”
Dimitris Kalimeris et al · 2019
Later among the works it cites.
“Phase transition in the spiked random tensor with rademacher prior”
Wei-Kuo Chen · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
“Matrix analysis”
Roger Horn and Charles Johnson · 2012
Cited alongside, same era.
“Invariant scattering convolution networks”
J. Bruna and S. Mallat · 2013
Cited alongside, same era.
“Random matrices and complexity of spin glasses”
A. Auffinger, G. Ben and J. Cerny · 2013
Cited alongside, same era.
“Performance-optimized hierarchical models predict neural responses in higher visual cortex”
Daniel.. Yamins et al · 2014
Cited alongside, same era.
“Neural processing of natural sounds”
Frédéric. Theunissen and Julie. Elie · 2014
Cited alongside, same era.
“Deep learning in neural networks: An overview”
Jürgen Schmidhuber · 2014
Cited alongside, same era.
“Recurrent neural networks learn robust representations by dynamically balancing compression and expansion” bioRxiv
Matthew Farrell et al · 2019
Later among the works it cites.
“Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup”
Sebastian Goldt et al · 2019
Later among the works it cites.
“TensorLy: Tensor Learning in Python”
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar and Maja Pantic · 2019
Later among the works it cites.
“MATLAB Tensor Toolbox Version 3.2”, https://www.tensortoolbox.org/, 2019
B.. Bader, T.. Kolda and et al · 2019
Later among the works it cites.
“Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”
Kohitij Kar and James. DiCarlo · 2020
Later among the works it cites.
“‘Place-cell’ emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space”
Moshir Harsh, Jérôme Tubiana, Simona Cocco and Rémi Monasson · 2020
Later among the works it cites.
“Towards Learning Convolutions from Scratch”
Behnam Neyshabur · 2020
Later among the works it cites.
“High-dimensional dynamics of generalization error in neural networks”
Madhu. Advani, Andrew. Saxe and Haim Sompolinsky · 2020
Later among the works it cites.
“Modeling the influence of data structure on learning in neural networks: The hidden manifold model”
S. Goldt, M. Mézard, F. Krzakala and L. Zdeborová · 2020
Later among the works it cites.
“Universality laws for high-dimensional learning with random features” arXiv:2009.07669, 2020
Hong Hu and Yue Lu · 2020
Later among the works it cites.
“Statistical limits of spiked tensor models”
Amelia Perry, Alexander Wein and Afonso Bandeira · 2020
Later among the works it cites.
“A First Course in Random Matrix Theory: for Physicists, Engineers and Data Scientists”
Marc Potters and Jean-Philippe Bouchaud · 2020
Later among the works it cites.
“Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision”
Courtney. Spoerer et al · 2020
Later among the works it cites.
“Going in circles is the way forward: the role of recurrence in visual inference”
Ruben van Bergen and Nikolaus Kriegeskorte · 2020
Later among the works it cites.
“Estimating Higher-Order Moments Using Symmetric Tensor Decomposition”
Samantha Sherman and Tamara. Kolda · 2020
Later among the works it cites.
“Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges”, 2021
Michael. Bronstein, Joan Bruna, Taco Cohen and Petar Veličković · 2021
Later among the works it cites.
“Place cells may simply be memory cells: Memory compression leads to spatial tuning and history dependence”
Marcus. Benna and Stefano Fusi · 2021
Later among the works it cites.
“Autoencoder networks extract latent variables and encode these variables in their connectomes”
Matthew Farrell et al · 2021
Later among the works it cites.
“Learning with invariances in random features and kernel models”, 2021
Song Mei, Theodor Misiakiewicz and Andrea Montanari · 2021
Later among the works it cites.
“Learning with convolution and pooling operations in kernel methods”, 2021
Theodor Misiakiewicz and Song Mei · 2021
Later among the works it cites.
“Locality defeats the curse of dimensionality in convolutional teacher-student scenarios”
Alessandro Favero, Francesco Cagnetta and Matthieu Wyart · 2021
Later among the works it cites.
“Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed”
Maria Refinetti, Sebastian Goldt, Florent Krzakala and Lenka Zdeborova · 2021
Later among the works it cites.
“The Gaussian equivalence of generative models for learning with two-layer neural networks”
S. Goldt et al · 2021
Later among the works it cites.
“Learning curves of generic features maps for realistic datasets with a teacher-student model”
Bruno Loureiro et al · 2021
Later among the works it cites.
“Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity”
Gabriel Ocker and Michael. Buice · 2021
Later among the works it cites.
Franco Pellegrini and Giulio Biroli · 2021
Later among the works it cites.
“A Random Matrix Perspective on Random Tensors”, 2021
José de Morais, Romain Couillet and Pierre Comon · 2021
Later among the works it cites.
“The Bootstrap Framework: Generalization Through the Lens of Online Optimization”
Preetum Nakkiran, Behnam Neyshabur and Hanie Sedghi · 2021
Later among the works it cites.
Rodrigo Veiga et al · 2022
Closest in time.