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To understand deep learning we need to understand kernel learning

5 February 2018

Papers citing "To understand deep learning we need to understand kernel learning"

50 / 271 papers shown

Title
Label-Imbalanced and Group-Sensitive Classification under Overparameterization Ganesh Ramachandra Kini Orestis Paraskevas Samet Oymak Christos Thrampoulidis 129 96 0 02 Mar 2021
Neural Generalization of Multiple Kernel Learning Ahamad Navid Ghanizadeh Kamaledin Ghiasi-Shirazi R. Monsefi Mohammadreza Qaraei 25 2 0 26 Feb 2021
On the Inherent Regularization Effects of Noise Injection During Training Oussama Dhifallah Yue M. Lu 61 30 0 15 Feb 2021
Deep Learning Generalization and the Convex Hull of Training Sets Roozbeh Yousefzadeh 67 20 0 25 Jan 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor Rong Jin 80 3 0 12 Jan 2021
Benign overfitting without concentration Zongyuan Shang MLT 32 1 0 04 Jan 2021
Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks Xiangyu Chang Yingcong Li Samet Oymak Christos Thrampoulidis 86 51 0 16 Dec 2020
On Generalization of Adaptive Methods for Over-parameterized Linear Regression Vatsal Shah Soumya Basu Anastasios Kyrillidis Sujay Sanghavi AI4CE 59 4 0 28 Nov 2020
Metric Transforms and Low Rank Matrices via Representation Theory of the Real Hyperrectangle Josh Alman T. Chu Gary Miller Shyam Narayanan Mark Sellke Zhao Song 38 1 0 23 Nov 2020
Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting and Regularization Ke Wang Christos Thrampoulidis 98 29 0 18 Nov 2020
Understanding Double Descent Requires a Fine-Grained Bias-Variance Decomposition Ben Adlam Jeffrey Pennington UD 121 93 0 04 Nov 2020
Kernel Dependence Network Chieh-Tsai Wu A. Masoomi Arthur Gretton Jennifer Dy 18 0 0 04 Nov 2020
Which Minimizer Does My Neural Network Converge To? Manuel Nonnenmacher David Reeb Ingo Steinwart ODL 32 4 0 04 Nov 2020
Over-parametrized neural networks as under-determined linear systems Austin R. Benson Anil Damle Alex Townsend 11 0 0 29 Oct 2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training Adel Javanmard Mahdi Soltanolkotabi AAML 110 63 0 21 Oct 2020
The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers Preetum Nakkiran Behnam Neyshabur Hanie Sedghi OffRL 97 11 0 16 Oct 2020
On the Universality of the Double Descent Peak in Ridgeless Regression David Holzmüller 98 13 0 05 Oct 2020
Deep Equals Shallow for ReLU Networks in Kernel Regimes A. Bietti Francis R. Bach 110 90 0 30 Sep 2020
Experimental Design for Overparameterized Learning with Application to Single Shot Deep Active Learning N. Shoham H. Avron BDL 39 12 0 27 Sep 2020
Deep Neural Tangent Kernel and Laplace Kernel Have the Same RKHS Lin Chen Sheng Xu 193 94 0 22 Sep 2020
A Principle of Least Action for the Training of Neural Networks Skander Karkar Ibrahhim Ayed Emmanuel de Bézenac Patrick Gallinari AI4CE 69 10 0 17 Sep 2020
Distributional Generalization: A New Kind of Generalization Preetum Nakkiran Yamini Bansal OOD 85 42 0 17 Sep 2020
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training Tianle Cai Shengjie Luo Keyulu Xu Di He Tie-Yan Liu Liwei Wang GNN 108 167 0 07 Sep 2020
Extreme Memorization via Scale of Initialization Harsh Mehta Ashok Cutkosky Behnam Neyshabur 60 20 0 31 Aug 2020
$β$ -Variational Classifiers Under Attack Marco Maggipinto M. Terzi Gian Antonio Susto AAML OOD 27 1 0 20 Aug 2020
The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization Ben Adlam Jeffrey Pennington 61 125 0 15 Aug 2020
Do ideas have shape? Idea registration as the continuous limit of artificial neural networks H. Owhadi 155 14 0 10 Aug 2020
A Survey on Large-scale Machine Learning Meng Wang Weijie Fu Xiangnan He Shijie Hao Xindong Wu 84 112 0 10 Aug 2020
What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation Vitaly Feldman Chiyuan Zhang TDI 248 472 0 09 Aug 2020
Benign Overfitting and Noisy Features Zhu Li Weijie Su Dino Sejdinovic 63 22 0 06 Aug 2020
Multiple Descent: Design Your Own Generalization Curve Lin Chen Yifei Min M. Belkin Amin Karbasi DRL 162 61 0 03 Aug 2020
Implicit Regularization via Neural Feature Alignment A. Baratin Thomas George César Laurent R. Devon Hjelm Guillaume Lajoie Pascal Vincent Simon Lacoste-Julien 73 6 0 03 Aug 2020
A finite sample analysis of the benign overfitting phenomenon for ridge function estimation E. Caron Stéphane Chrétien MLT 67 6 0 25 Jul 2020
Prediction in latent factor regression: Adaptive PCR and beyond Xin Bing F. Bunea Seth Strimas-Mackey M. Wegkamp 47 2 0 20 Jul 2020
How benign is benign overfitting? Amartya Sanyal P. Dokania Varun Kanade Philip Torr NoLa AAML 89 58 0 08 Jul 2020
On the Similarity between the Laplace and Neural Tangent Kernels Amnon Geifman A. Yadav Yoni Kasten Meirav Galun David Jacobs Ronen Basri 155 96 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 170 190 0 23 Jun 2020
Exploring Weight Importance and Hessian Bias in Model Pruning Mingchen Li Yahya Sattar Christos Thrampoulidis Samet Oymak 71 4 0 19 Jun 2020
Revisiting minimum description length complexity in overparameterized models Raaz Dwivedi Chandan Singh Bin Yu Martin J. Wainwright 63 5 0 17 Jun 2020
Interpolation and Learning with Scale Dependent Kernels Nicolò Pagliana Alessandro Rudi Ernesto De Vito Lorenzo Rosasco 91 8 0 17 Jun 2020
Using Wavelets and Spectral Methods to Study Patterns in Image-Classification Datasets Roozbeh Yousefzadeh Furong Huang 51 6 0 17 Jun 2020
To Each Optimizer a Norm, To Each Norm its Generalization Sharan Vaswani Reza Babanezhad Jose Gallego Aaron Mishkin Simon Lacoste-Julien Nicolas Le Roux 66 8 0 11 Jun 2020
Asymptotics of Ridge (less) Regression under General Source Condition Dominic Richards Jaouad Mourtada Lorenzo Rosasco 88 73 0 11 Jun 2020
On Uniform Convergence and Low-Norm Interpolation Learning Lijia Zhou Danica J. Sutherland Nathan Srebro 69 30 0 10 Jun 2020
Double Descent Risk and Volume Saturation Effects: A Geometric Perspective Prasad Cheema M. Sugiyama 113 3 0 08 Jun 2020
Learning from Non-Random Data in Hilbert Spaces: An Optimal Recovery Perspective S. Foucart Chunyang Liao Shahin Shahrampour Yinsong Wang 34 0 0 05 Jun 2020
Quantifying the Uncertainty of Precision Estimates for Rule based Text Classifiers J. Nutaro Özgür Özmen 23 0 0 19 May 2020
Classification vs regression in overparameterized regimes: Does the loss function matter? Vidya Muthukumar Adhyyan Narang Vignesh Subramanian M. Belkin Daniel J. Hsu A. Sahai 114 151 0 16 May 2020
Provable Robust Classification via Learned Smoothed Densities Saeed Saremi R. Srivastava AAML 85 3 0 09 May 2020
Mathematical foundations of stable RKHSs M. Bisiacco G. Pillonetto 17 26 0 06 May 2020