Daniel Kunin

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

Daniel Kunin

Stanford University

Verified email at stanford.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	765	2020
Loss landscapes of regularized linear autoencoders D Kunin, J Bloom, A Goeva, C Seed International Conference on Machine Learning (ICML) Oral, 2019	114	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	83	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	63*	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	43	2020
Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks H Tanaka, D Kunin Neural Information Processing Systems (NeurIPS), 2021	39*	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	29	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	22*	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	10	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	3	2024
A Quasistatic Derivation of Optimization Algorithms' Exploration on Minima Manifolds C Ma, D Kunin, L Ying

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