Daniel Kunin

Citované v

	Všetky	Od 2020
Citácie	1172	1157
h-index	9	9
i10-index	9	9

380

190

285

201920202021202220232024202510 35 149 227 329 362 52

Verejný prístup

všetky položky

4 články

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Na základe mandátov na financovanie

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Surya GanguliAssociate Professor, Stanford UniversityOverená e-mailová adresa na: stanford.edu
Daniel YaminsAssociate Professor of Computer Science and of Psychology, Stanford UniversityOverená e-mailová adresa na: stanford.edu
Hidenori TanakaGroup Leader, CBS-NTT Physics of Intelligence Program, Harvard UniversityOverená e-mailová adresa na: fas.harvard.edu
Javier Sagastuy-BrenaStanford UniversityOverená e-mailová adresa na: stanford.edu
Ma ChaoDepartment of Mathematics, Stanford UniversityOverená e-mailová adresa na: stanford.edu
Lexing YingProfessor of Mathematics, Stanford UniversityOverená e-mailová adresa na: stanford.edu
Lei Wu (吴磊)Assistant Professor, Peking UniversityOverená e-mailová adresa na: math.pku.edu.cn
Aleksandrina GoevaUniversity of TorontoOverená e-mailová adresa na: broadinstitute.org
Cotton SeedBroad Institute of MIT and HarvardOverená e-mailová adresa na: alum.mit.edu
Aran NayebiAssistant Professor of Machine Learning, Carnegie Mellon University (CMU)Overená e-mailová adresa na: cs.cmu.edu
Eshed MargalitStanford UniversityOverená e-mailová adresa na: stanford.edu
Lauren E. GillespieComputer Science PhD Student, Stanford UniversityOverená e-mailová adresa na: cs.stanford.edu

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Daniel Kunin

Stanford University

Overená e-mailová adresa na: stanford.edu - Domovská stránka


Názov Zoradiť podľa citácií Zoradiť podľa roka Zoradiť podľa názvu	Citované v Citované v	Rok
Pruning neural networks without any data by iteratively conserving synaptic flow H Tanaka, D Kunin, DL Yamins, S Ganguli Neural Information Processing Systems (NeurIPS), 2020	753	2020
Loss landscapes of regularized linear autoencoders D Kunin, J Bloom, A Goeva, C Seed International Conference on Machine Learning (ICML) Oral, 2019	113	2019
Neural mechanics: Symmetry and broken conservation laws in deep learning dynamics D Kunin, J Sagastuy-Brena, S Ganguli, DLK Yamins, H Tanaka International Conference on Learning Representations (ICLR), 2020	81	2020
Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes C Ma, D Kunin, L Wu, L Ying Journal of Machine Learning Research, 2022	61*	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 (ICML), 2020	42	2020
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks H Tanaka, D Kunin Neural Information Processing Systems (NeurIPS), 2021	38*	2021
The asymmetric maximum margin bias of quasi-homogeneous neural networks D Kunin, A Yamamura, C Ma, S Ganguli International Conference on Learning Representations (ICLR) Spotlight, 2022	28	2022
Stochastic collapse: How gradient noise attracts sgd dynamics towards simpler subnetworks F Chen, D Kunin, A Yamamura*, S Ganguli Journal of Statistical Mechanics: Theory and Experiment, 2024	27	2024
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	20*	2023
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 Neural Information Processing Systems (NeurIPS) Spotlight, 2024	7	2024
From lazy to rich: Exact learning dynamics in deep linear networks CCJ Dominé, N Anguita, AM Proca, L Braun, D Kunin, PAM Mediano, ... arXiv preprint arXiv:2409.14623, 2024	2	2024
A Quasistatic Derivation of Optimization Algorithms' Exploration on Minima Manifolds C Ma, D Kunin, L Ying

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