Locality defeats the curse of dimensionality in convolutional teacher-student scenarios

16 June 2021

Papers citing "Locality defeats the curse of dimensionality in convolutional teacher-student scenarios"

28 / 28 papers shown

Title
Learning curves theory for hierarchically compositional data with power-law distributed features Francesco Cagnetta Hyunmo Kang Matthieu Wyart 100 1 0 11 May 2025
Learning with invariances in random features and kernel models Song Mei Theodor Misiakiewicz Andrea Montanari OOD 96 90 0 25 Feb 2021
Computational Separation Between Convolutional and Fully-Connected Networks Eran Malach Shai Shalev-Shwartz 67 26 0 03 Oct 2020
Towards Learning Convolutions from Scratch Behnam Neyshabur SSL 283 71 0 27 Jul 2020
On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman A. Yadav Yoni Kasten Meirav Galun David Jacobs Ronen Basri 115 94 0 03 Jul 2020
Spectral Bias and Task-Model Alignment Explain Generalization in Kernel Regression and Infinitely Wide Neural Networks Abdulkadir Canatar Blake Bordelon Cengiz Pehlevan 97 187 0 23 Jun 2020
Kernel Alignment Risk Estimator: Risk Prediction from Training Data Arthur Jacot Berfin cSimcsek Francesco Spadaro Clément Hongler Franck Gabriel 68 68 0 17 Jun 2020
Implicit Regularization of Random Feature Models Arthur Jacot Berfin Simsek Francesco Spadaro Clément Hongler Franck Gabriel 64 83 0 19 Feb 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 223 206 0 07 Feb 2020
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 112 164 0 25 Aug 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 86 259 0 29 May 2019
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm S. Spigler Mario Geiger Matthieu Wyart 68 38 0 26 May 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 223 925 0 26 Apr 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 211 1,104 0 18 Feb 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 835 0 19 Dec 2018
A jamming transition from under- to over-parametrization affects loss landscape and generalization S. Spigler Mario Geiger Stéphane dÁscoli Levent Sagun Giulio Biroli Matthieu Wyart 61 150 0 22 Oct 2018
Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Roman Novak Lechao Xiao Jaehoon Lee Yasaman Bahri Greg Yang Jiri Hron Daniel A. Abolafia Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 65 309 0 11 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 219 1,272 0 04 Oct 2018
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences Motonobu Kanagawa Philipp Hennig Dino Sejdinovic Bharath K. Sriperumbudur GP BDL 133 342 0 06 Jul 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 267 3,203 0 20 Jun 2018
Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty Hao Zhou Yunyang Xiong Vikas Singh UQCV BDL 65 4 0 10 Jun 2018
Gaussian Process Behaviour in Wide Deep Neural Networks A. G. Matthews Mark Rowland Jiri Hron Richard Turner Zoubin Ghahramani BDL 144 559 0 30 Apr 2018
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups Risi Kondor Shubhendu Trivedi MLT 112 498 0 11 Feb 2018
Deep Learning Scaling is Predictable, Empirically Joel Hestness Sharan Narang Newsha Ardalani G. Diamos Heewoo Jun Hassan Kianinejad Md. Mostofa Ali Patwary Yang Yang Yanqi Zhou 92 741 0 01 Dec 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 125 1,093 0 01 Nov 2017
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review T. Poggio H. Mhaskar Lorenzo Rosasco Brando Miranda Q. Liao 97 576 0 02 Nov 2016
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks Julien Mairal SSL 64 130 0 20 May 2016
Breaking the Curse of Dimensionality with Convex Neural Networks Francis R. Bach 184 706 0 30 Dec 2014