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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
Training NTK to Generalize with KARE Johannes Schwab Bryan Kelly Semyon Malamud Teng Andrea Xu 149 0 0 16 May 2025
Deep Learning is Not So Mysterious or Different Andrew Gordon Wilson 101 6 0 03 Mar 2025
Feature maps for the Laplacian kernel and its generalizations Sudhendu Ahir Parthe Pandit 101 0 0 24 Feb 2025
Understanding Generalization in Transformers: Error Bounds and Training Dynamics Under Benign and Harmful Overfitting Yingying Zhang Zhikai Wu Jian Li Yang Liu MLT AI4CE 62 1 0 18 Feb 2025
Learning Theory for Kernel Bilevel Optimization Fares El Khoury Edouard Pauwels Samuel Vaiter Michael Arbel 448 0 0 12 Feb 2025
Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation Lingkun Luo Shiqiang Hu Liming Chen 174 0 0 10 Feb 2025
A theoretical framework for overfitting in energy-based modeling Giovanni Catania A. Decelle Cyril Furtlehner Beatriz Seoane 183 2 0 31 Jan 2025
Image Classification with Deep Reinforcement Active Learning Mingyuan Jiu Xuguang Song H. Sahbi Shupan Li Yan Chen Wei Guo Lihua Guo Mingliang Xu VLM 56 1 0 31 Dec 2024
Towards Simple and Provable Parameter-Free Adaptive Gradient Methods Yuanzhe Tao Huizhuo Yuan Xun Zhou Yuan Cao Q. Gu ODL 66 0 0 27 Dec 2024
Fast training of large kernel models with delayed projections Amirhesam Abedsoltan Siyuan Ma Parthe Pandit Mikhail Belkin 119 1 0 25 Nov 2024
Infinite Width Limits of Self Supervised Neural Networks Maximilian Fleissner Gautham Govind Anil Debarghya Ghoshdastidar SSL 465 1 0 17 Nov 2024
Double Descent Meets Out-of-Distribution Detection: Theoretical Insights and Empirical Analysis on the role of model complexity Mouin Ben Ammar David Brellmann Arturo Mendoza Antoine Manzanera Gianni Franchi OODD 116 0 0 04 Nov 2024
Theoretical Limitations of Ensembles in the Age of Overparameterization Niclas Dern John P. Cunningham Geoff Pleiss BDL UQCV 92 1 0 21 Oct 2024
On Goodhart's law, with an application to value alignment El-Mahdi El-Mhamdi Lê-Nguyên Hoang 43 0 0 12 Oct 2024
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs Alicia Curth 59 3 0 27 Sep 2024
Input Space Mode Connectivity in Deep Neural Networks Jakub Vrabel Ori Shem-Ur Yaron Oz David Krueger 110 1 0 09 Sep 2024
Can We Theoretically Quantify the Impacts of Local Updates on the Generalization Performance of Federated Learning? Peizhong Ju Haibo Yang Jia Liu Yingbin Liang Ness B. Shroff FedML 80 0 0 05 Sep 2024
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual Learning Mohammadamin Banayeeanzade Mahdi Soltanolkotabi Mohammad Rostami CLL LRM 311 4 0 29 Aug 2024
Generalization bounds for regression and classification on adaptive covering input domains Wen-Liang Hwang 67 0 0 29 Jul 2024
Geometrically Inspired Kernel Machines for Collaborative Learning Beyond Gradient Descent Mohit Kumar Alexander Valentinitsch Magdalena Fuchs Mathias Brucker Juliana Bowles Adnan Husaković Ali Abbas Bernhard A. Moser 109 0 0 05 Jul 2024
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis Zhang Chen Christian Scano Srishti Gupta Xiaoyi Feng Zhaoqiang Xia ... Maura Pintor Luca Oneto Ambra Demontis Battista Biggio Fabio Roli AAML 87 2 0 14 Jun 2024
Precise analysis of ridge interpolators under heavy correlations -- a Random Duality Theory view Mihailo Stojnic 49 1 0 13 Jun 2024
Ridge interpolators in correlated factor regression models -- exact risk analysis Mihailo Stojnic 57 1 0 13 Jun 2024
Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning Fan He Mingzhe He Lei Shi Xiaolin Huang Johan A. K. Suykens 68 1 0 03 Jun 2024
Machine Learning Robustness: A Primer Houssem Ben Braiek Foutse Khomh AAML OOD 106 8 0 01 Apr 2024
On the Benefits of Over-parameterization for Out-of-Distribution Generalization Yifan Hao Yong Lin Difan Zou Tong Zhang OODD OOD 88 6 0 26 Mar 2024
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization Yutong Wang Rishi Sonthalia Wei Hu 108 5 0 12 Mar 2024
Benign overfitting in leaky ReLU networks with moderate input dimension Kedar Karhadkar Erin E. George Michael Murray Guido Montúfar Deanna Needell MLT 85 2 0 11 Mar 2024
From Zero to Hero: How local curvature at artless initial conditions leads away from bad minima Tony Bonnaire Giulio Biroli C. Cammarota 115 0 0 04 Mar 2024
Model Collapse Demystified: The Case of Regression Elvis Dohmatob Yunzhen Feng Julia Kempe 104 38 0 12 Feb 2024
The Optimality of Kernel Classifiers in Sobolev Space Jianfa Lai Zhifan Li Dongming Huang Qian Lin 64 1 0 02 Feb 2024
Spectrally Transformed Kernel Regression Runtian Zhai Rattana Pukdee Roger Jin Maria-Florina Balcan Pradeep Ravikumar BDL 74 2 0 01 Feb 2024
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness Yifan Hao Tong Zhang AAML 144 5 0 19 Jan 2024
MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World Zheng Zhou Hong Zhao Ju Liu Qiaosheng Zhang Liwei Geng Shuchang Lyu W. Feng AAML 80 2 0 29 Dec 2023
Critical Influence of Overparameterization on Sharpness-aware Minimization Sungbin Shin Dongyeop Lee Maksym Andriushchenko Namhoon Lee AAML 160 2 0 29 Nov 2023
Applying statistical learning theory to deep learning Cédric Gerbelot Avetik G. Karagulyan Stefani Karp Kavya Ravichandran Menachem Stern Nathan Srebro FedML 53 2 0 26 Nov 2023
Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization Adel Javanmard Vahab Mirrokni 97 2 0 06 Oct 2023
Optimal Nonlinearities Improve Generalization Performance of Random Features Samet Demir Zafer Dogan MLT 27 2 0 28 Sep 2023
A Spectral Theory of Neural Prediction and Alignment Abdulkadir Canatar J. Feather Albert J. Wakhloo SueYeon Chung OOD 77 15 0 22 Sep 2023
How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent Mike Nguyen Nicole Mücke MLT 84 6 0 14 Sep 2023
Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding Shaik Basheeruddin Shah Pradyumna Pradhan Wei Pu Ramunaidu Randhi Miguel R. D. Rodrigues Yonina C. Eldar 82 4 0 12 Sep 2023
An Adaptive Tangent Feature Perspective of Neural Networks Daniel LeJeune Sina Alemohammad 68 1 0 29 Aug 2023
Six Lectures on Linearized Neural Networks Theodor Misiakiewicz Andrea Montanari 137 13 0 25 Aug 2023
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness Patrick Butlin R. Long Eric Elmoznino Yoshua Bengio Jonathan C. P. Birch ... L. Mudrik Megan A. K. Peters Eric Schwitzgebel Jonathan Simon Rufin VanRullen LLMAG 85 107 0 17 Aug 2023
Size Lowerbounds for Deep Operator Networks Anirbit Mukherjee Amartya Roy AI4CE 70 3 0 11 Aug 2023
Large Language Models Michael R Douglas LLMAG LM&MA 177 645 0 11 Jul 2023
Solving Kernel Ridge Regression with Gradient-Based Optimization Methods Oskar Allerbo 14 1 0 29 Jun 2023
Training shallow ReLU networks on noisy data using hinge loss: when do we overfit and is it benign? Erin E. George Michael Murray W. Swartworth Deanna Needell MLT 61 5 0 16 Jun 2023
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes Peizhong Ju Sen Lin M. Squillante Yitao Liang Ness B. Shroff 54 2 0 08 Jun 2023
Continual Learning in Linear Classification on Separable Data Itay Evron E. Moroshko G. Buzaglo M. Khriesh B. Marjieh Nathan Srebro Daniel Soudry CLL 72 17 0 06 Jun 2023