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Neural tangent kernel: convergence and generalization in neural networks (invited paper)

Published: 15 June 2021 Publication History

Abstract

The Neural Tangent Kernel is a new way to understand the gradient descent in deep neural networks, connecting them with kernel methods. In this talk, I'll introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how they give us insight into the dynamics of neural networks during training and into their generalization features.

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  • (2025)Towards NeuroAI: introducing neuronal diversity into artificial neural networksMed-X10.1007/s44258-024-00042-23:1Online publication date: 9-Jan-2025
  • (2024)Convergence rates for shallow neural networks learned by gradient descentBernoulli10.3150/23-BEJ160530:1Online publication date: 1-Feb-2024
  • (2024)Privacy-Preserving Constrained Domain Generalization Via Gradient AlignmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331527936:5(2142-2150)Online publication date: May-2024
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  1. Neural tangent kernel: convergence and generalization in neural networks (invited paper)

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        cover image ACM Conferences
        STOC 2021: Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing
        June 2021
        1797 pages
        ISBN:9781450380539
        DOI:10.1145/3406325
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        Published: 15 June 2021

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        1. Neural Tangent Kernel

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        Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

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        Cited By

        View all
        • (2025)Towards NeuroAI: introducing neuronal diversity into artificial neural networksMed-X10.1007/s44258-024-00042-23:1Online publication date: 9-Jan-2025
        • (2024)Convergence rates for shallow neural networks learned by gradient descentBernoulli10.3150/23-BEJ160530:1Online publication date: 1-Feb-2024
        • (2024)Privacy-Preserving Constrained Domain Generalization Via Gradient AlignmentIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331527936:5(2142-2150)Online publication date: May-2024
        • (2024)A hyperparameter study for quantum kernel methodsQuantum Machine Intelligence10.1007/s42484-024-00172-16:2Online publication date: 15-Jul-2024
        • (2024)A geometrical viewpoint on the benign overfitting property of the minimum $$\ell _2$$-norm interpolant estimator and its universalityProbability Theory and Related Fields10.1007/s00440-024-01336-7Online publication date: 8-Nov-2024
        • (2023)GeoINR 1.0: an implicit neural network approach to three-dimensional geological modellingGeoscientific Model Development10.5194/gmd-16-6987-202316:23(6987-7012)Online publication date: 29-Nov-2023
        • (2023)Quantum Lazy TrainingQuantum10.22331/q-2023-04-27-9897(989)Online publication date: 27-Apr-2023
        • (2023)ROMO: Retrieval-enhanced Offline Model-based OptimizationProceedings of the Fifth International Conference on Distributed Artificial Intelligence10.1145/3627676.3627685(1-9)Online publication date: 30-Nov-2023
        • (2023)Machine Unlearning: A SurveyACM Computing Surveys10.1145/360362056:1(1-36)Online publication date: 28-Aug-2023
        • (2023)3D Neural Sculpting (3DNS): Editing Neural Signed Distance Functions2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV56688.2023.00450(4510-4519)Online publication date: Jan-2023
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