Boris Hanin

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David RolnickMcGill University, Mila Quebec AI InstituteVerified email at cs.mcgill.ca
Ronald DeVoreProfessor of Matematics, Texas A&M UniversityVerified email at math.tamu.edu
Guergana PetrovaTexas A&M UniversityVerified email at tamu.edu
Yaiza CanzaniUniversity of North Carolina at Chapel HillVerified email at email.unc.edu
Mihai NicaUniversity of GuelphVerified email at uoguelph.ca
Steve ZelditchWayne and Elizabeth Jones Professor of Mathematics, Northwestern UniversityVerified email at math.northwestern.edu
Sho YaidaFAIR at MetaVerified email at meta.com
Daniel A. RobertsResearch Affiliate @ MIT, AI Fellow @ SequoiaVerified email at mit.edu
Mark SellkeAssistant Professor of Statistics, Harvard UniversityVerified email at fas.harvard.edu
Simon FoucartProfessor of Mathematics, Texas A&M UniversityVerified email at tamu.edu
Ingrid DaubechiesDuke UniversityVerified email at math.duke.edu
Peng ZhouUC Berkeley, Math DepartmentVerified email at berkeley.edu
Yi SunAssistant Professor, UChicago StatisticsVerified email at statistics.uchicago.edu
Ryan JeongVerified email at cam.ac.uk
Grigoris PaourisProfessor in Mathematics, Texas A&M UniversityVerified email at math.tamu.edu
Erik LundbergFlorida Atlantic UniversityVerified email at fau.edu
Zhangyang (Atlas) WangUniversity of Texas at Austin & Picsart AI ResearchVerified email at utexas.edu
Wuyang ChenIncoming Assistant Professor, CS@Simon Fraser University; PostDoc, Stats@UC BerkeleyVerified email at utexas.edu
Leonid HaninIdaho State UniversityVerified email at isu.edu

Boris Hanin

Assistant Professor, Princeton ORFE

Verified email at princeton.edu - Homepage

probability mathematical physics deep learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Universal function approximation by deep neural nets with bounded width and relu activations B Hanin Mathematics 7 (10), 992, 2019	360	2019
Which neural net architectures give rise to exploding and vanishing gradients? B Hanin Advances in neural information processing systems 31, 2018	257	2018
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems 31, 2018	244	2018
The principles of deep learning theory DA Roberts, S Yaida, B Hanin Cambridge University Press, 2022	204	2022
Approximating continuous functions by relu nets of minimal width B Hanin, M Sellke arXiv preprint arXiv:1710.11278, 2017	200	2017
Nonlinear Approximation and (Deep) Networks I Daubechies, R DeVore, S Foucart, B Hanin, G Petrova Constructive Approximation 55 (1), 127-172, 2022	199	2022
Deep relu networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in neural information processing systems 32, 2019	190	2019
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	188	2019
Neural network approximation R DeVore, B Hanin, G Petrova Acta Numerica 30, 327-444, 2021	143	2021
Finite depth and width corrections to the neural tangent kernel B Hanin, M Nica arXiv preprint arXiv:1909.05989, 2019	134	2019
Products of many large random matrices and gradients in deep neural networks B Hanin, M Nica Communications in Mathematical Physics 376 (1), 287-322, 2020	68	2020
Scaling limit for the kernel of the spectral projector and remainder estimates in the pointwise Weyl law Y Canzani, B Hanin Analysis & PDE 8 (7), 1707-1731, 2015	42	2015
Scaling limit for the kernel of the spectral projector and remainder estimates in the pointwise Weyl law Y Canzani, B Hanin Analysis & PDE 8 (7), 1707-1731, 2015	42	2015
Correlations and pairing between zeros and critical points of Gaussian random polynomials B Hanin International Mathematics Research Notices 2015 (2), 381-421, 2015	29	2015
Local universality for zeros and critical points of monochromatic random waves Y Canzani, B Hanin Communications in Mathematical Physics 378 (3), 1677-1712, 2020	27	2020
Pairing of zeros and critical points for random polynomials B Hanin	27	2017
Nodal sets of random eigenfunctions for the isotropic harmonic oscillator B Hanin, S Zelditch, P Zhou International Mathematics Research Notices 2015 (13), 4813-4839, 2015	24	2015
Random neural networks in the infinite width limit as Gaussian processes B Hanin arXiv preprint arXiv:2107.01562, 2021	23	2021
Scaling Asymptotics for the Spectral Projector of the Laplacian Y Canzani, B Hanin The Journal of Geometric Analysis 28, 111-122, 2018	22	2018
Correlation functions in random fully connected neural networks at finite width B Hanin arXiv preprint arXiv:2204.01058, 2022	21	2022

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