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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
Generalization Error of Generalized Linear Models in High Dimensions M Motavali Emami Mojtaba Sahraee-Ardakan Parthe Pandit S. Rangan A. Fletcher AI4CE 59 39 0 01 May 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 130 176 0 23 Apr 2020
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks Tengyuan Liang Hai Tran-Bach 48 11 0 09 Apr 2020
Convolutional Spectral Kernel Learning Jian Li Yong Liu Weiping Wang BDL 29 5 0 28 Feb 2020
Rethinking Bias-Variance Trade-off for Generalization of Neural Networks Zitong Yang Yaodong Yu Chong You Jacob Steinhardt Yi-An Ma 101 186 0 26 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 104 229 0 25 Feb 2020
Precise Tradeoffs in Adversarial Training for Linear Regression Adel Javanmard Mahdi Soltanolkotabi Hamed Hassani AAML 83 109 0 24 Feb 2020
Diversity sampling is an implicit regularization for kernel methods Michaël Fanuel J. Schreurs Johan A. K. Suykens 63 14 0 20 Feb 2020
Characterizing Structural Regularities of Labeled Data in Overparameterized Models Ziheng Jiang Chiyuan Zhang Kunal Talwar Michael C. Mozer TDI 68 104 0 08 Feb 2020
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks Blake Bordelon Abdulkadir Canatar Cengiz Pehlevan 284 208 0 07 Feb 2020
Interpolating Predictors in High-Dimensional Factor Regression F. Bunea Seth Strimas-Mackey M. Wegkamp 73 12 0 06 Feb 2020
$A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum-$\ell_1$-Norm Interpolated Classifiers$ A Precise High-Dimensional Asymptotic Theory for Boosting and Minimum- $\ell_1$ -Norm Interpolated Classifiers Tengyuan Liang Pragya Sur 133 70 0 05 Feb 2020
Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree Peizhong Ju Xiaojun Lin Jia Liu 131 7 0 02 Feb 2020
A Corrective View of Neural Networks: Representation, Memorization and Learning Guy Bresler Dheeraj M. Nagaraj MLT 77 18 0 01 Feb 2020
Risk of the Least Squares Minimum Norm Estimator under the Spike Covariance Model Yasaman Mahdaviyeh Zacharie Naulet 81 4 0 31 Dec 2019
Discriminative Clustering with Representation Learning with any Ratio of Labeled to Unlabeled Data Corinne Jones Vincent Roulet Zaïd Harchaoui 109 1 0 30 Dec 2019
The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks E. Weinan Chao Ma Lei Wu 42 14 0 15 Dec 2019
Double descent in the condition number T. Poggio Gil Kur Andy Banburski 67 27 0 12 Dec 2019
Exact expressions for double descent and implicit regularization via surrogate random design Michal Derezinski Feynman T. Liang Michael W. Mahoney 82 78 0 10 Dec 2019
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors Jeffrey Negrea Gintare Karolina Dziugaite Daniel M. Roy AI4CE 97 65 0 09 Dec 2019
A Model of Double Descent for High-dimensional Binary Linear Classification Zeyu Deng A. Kammoun Christos Thrampoulidis 126 148 0 13 Nov 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 149 46 0 15 Oct 2019
Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin Colin Wei Tengyu Ma AAML OOD 92 85 0 09 Oct 2019
The Role of Neural Network Activation Functions Rahul Parhi Robert D. Nowak 81 12 0 05 Oct 2019
On the Multiple Descent of Minimum-Norm Interpolants and Restricted Lower Isometry of Kernels Tengyuan Liang Alexander Rakhlin Xiyu Zhai 103 29 0 27 Aug 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 126 165 0 25 Aug 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 162 640 0 14 Aug 2019
Benign Overfitting in Linear Regression Peter L. Bartlett Philip M. Long Gábor Lugosi Alexander Tsigler MLT 126 780 0 26 Jun 2019
Does Learning Require Memorization? A Short Tale about a Long Tail Vitaly Feldman TDI 194 504 0 12 Jun 2019
Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation P. Mitra 83 50 0 09 Jun 2019
Deep Semi-Supervised Anomaly Detection Lukas Ruff Robert A. Vandermeulen Nico Görnitz Alexander Binder Emmanuel Müller K. Müller Marius Kloft UQCV 66 549 0 06 Jun 2019
Bad Global Minima Exist and SGD Can Reach Them Shengchao Liu Dimitris Papailiopoulos D. Achlioptas 91 81 0 06 Jun 2019
MaxiMin Active Learning in Overparameterized Model Classes} Mina Karzand Robert D. Nowak 54 20 0 29 May 2019
On the Inductive Bias of Neural Tangent Kernels A. Bietti Julien Mairal 128 260 0 29 May 2019
Interpretable deep Gaussian processes with moments Chi-Ken Lu Scott Cheng-Hsin Yang Xiaoran Hao Patrick Shafto 84 19 0 27 May 2019
Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration Kwang-Sung Jun Ashok Cutkosky Francesco Orabona 66 20 0 25 May 2019
Do Kernel and Neural Embeddings Help in Training and Generalization? Arman Rahbar Emilio Jorge Devdatt Dubhashi Morteza Haghir Chehreghani MLT 106 0 0 13 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 97 243 0 27 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 289 928 0 26 Apr 2019
Sparse Learning for Variable Selection with Structures and Nonlinearities Magda Gregorova 67 1 0 26 Mar 2019
Surprises in High-Dimensional Ridgeless Least Squares Interpolation Trevor Hastie Andrea Montanari Saharon Rosset Robert Tibshirani 302 747 0 19 Mar 2019
Shallow Neural Networks for Fluid Flow Reconstruction with Limited Sensors N. Benjamin Erichson L. Mathelin Z. Yao Steven L. Brunton Michael W. Mahoney J. Nathan Kutz AI4CE 61 34 0 20 Feb 2019
Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan J. Zico Kolter MoMe AI4CE 98 317 0 13 Feb 2019
Towards moderate overparameterization: global convergence guarantees for training shallow neural networks Samet Oymak Mahdi Soltanolkotabi 77 323 0 12 Feb 2019
KTBoost: Combined Kernel and Tree Boosting Fabio Sigrist 87 27 0 11 Feb 2019
Are All Layers Created Equal? Chiyuan Zhang Samy Bengio Y. Singer 111 140 0 06 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 153 158 0 04 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 234 974 0 24 Jan 2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits Xialiang Dou Tengyuan Liang MLT 83 42 0 21 Jan 2019
Consistency of Interpolation with Laplace Kernels is a High-Dimensional Phenomenon Alexander Rakhlin Xiyu Zhai 127 79 0 28 Dec 2018