Daniel Kunin 个人学术档案 - 学术资源搜索

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Surya GanguliAssociate Professor, Stanford University在 stanford.edu 的电子邮件经过验证
Daniel YaminsAssistant Professor of Computer Science and Psychology, Stanford University在 stanford.edu 的电子邮件经过验证
Hidenori TanakaGroup Leader, CBS-NTT Program in "Physics of Intelligence", Harvard University在 fas.harvard.edu 的电子邮件经过验证
Javier Sagastuy-BrenaStanford University在 stanford.edu 的电子邮件经过验证
Ma ChaoDepartment of Mathematics, Stanford University在 stanford.edu 的电子邮件经过验证
Lexing YingProfessor of Mathematics, Stanford University在 stanford.edu 的电子邮件经过验证
Lei Wu (吴磊)Assistant Professor, Peking University在 math.pku.edu.cn 的电子邮件经过验证
Aleksandrina GoevaBroad Institute在 broadinstitute.org 的电子邮件经过验证
Cotton SeedBroad Institute of MIT and Harvard在 alum.mit.edu 的电子邮件经过验证
Aran NayebiICoN Postdoctoral Fellow, MIT在 mit.edu 的电子邮件经过验证
Eshed MargalitStanford University在 stanford.edu 的电子邮件经过验证
Lauren GillespieComputer Science PhD Student, Stanford University在 cs.stanford.edu 的电子邮件经过验证

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Daniel Kunin

Stanford University

在 stanford.edu 的电子邮件经过验证 - 首页


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Pruning neural networks without any data by iteratively conserving synaptic flow H Tanaka, D Kunin, DL Yamins, S Ganguli Advances in neural information processing systems 33, 6377-6389, 2020	581	2020
Loss landscapes of regularized linear autoencoders D Kunin, J Bloom, A Goeva, C Seed International conference on machine learning, 3560-3569, 2019	96	2019
Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics D Kunin, J Sagastuy-Brena, S Ganguli, DLK Yamins, H Tanaka arXiv preprint arXiv:2012.04728, 2020	68	2020
Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes C Ma, D Kunin, L Wu, L Ying arXiv preprint arXiv:2204.11326, 2022	53*	2022
Two routes to scalable credit assignment without weight symmetry D Kunin, A Nayebi, J Sagastuy-Brena, S Ganguli, J Bloom, D Yamins International Conference on Machine Learning, 5511-5521, 2020	36	2020
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks H Tanaka, D Kunin	28*	2021
The asymmetric maximum margin bias of quasi-homogeneous neural networks D Kunin, A Yamamura, C Ma, S Ganguli arXiv preprint arXiv:2210.03820, 2022	18	2022
The Limiting Dynamics of SGD: Modified Loss, Phase-Space Oscillations, and Anomalous Diffusion D Kunin, J Sagastuy-Brena, L Gillespie, E Margalit, H Tanaka, S Ganguli, ... Neural Computation 36 (1), 151-174, 2023	17*	2023
Stochastic collapse: How gradient noise attracts sgd dynamics towards simpler subnetworks F Chen, D Kunin, A Yamamura, S Ganguli Advances in Neural Information Processing Systems 36, 2024	12	2024
Get rich quick: exact solutions reveal how unbalanced initializations promote rapid feature learning D Kunin, A Raventós, C Dominé, F Chen, D Klindt, A Saxe, S Ganguli arXiv preprint arXiv:2406.06158, 2024		2024
A Quasistatic Derivation of Optimization Algorithms' Exploration on Minima Manifolds C Ma, D Kunin, L Ying

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