Srinadh Bhojanapalli

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Behnam NeyshaburSenior Staff Research Scientist, DeepMindDirección de correo verificada de google.com
Nathan SrebroProfessor, TTIC and University of ChicagoDirección de correo verificada de ttic.edu
Sujay SanghaviAssociate Professor, Electrical and Computer Engineering, University of Texas, AustinDirección de correo verificada de mail.utexas.edu
Prateek JainGoogle Research IndiaDirección de correo verificada de google.com
Ayan ChakrabartiGoogle ResearchDirección de correo verificada de google.com
Anastasios KyrillidisAssociate Professor, Computer Science Department, Ken Kennedy Institute Fellow, Rice UniversityDirección de correo verificada de rice.edu
Rachel WardDirección de correo verificada de math.utexas.edu
Yudong ChenAssociate Professor, University of Wisconsin-MadisonDirección de correo verificada de wisc.edu
Zhiyuan LiAssistant Professor, Toyota Technological Institute at ChicagoDirección de correo verificada de ttic.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityDirección de correo verificada de cs.nyu.edu
Constantine CaramanisProfessor of Electrical and Computer Engineering, UT AustinDirección de correo verificada de utexas.edu
Dohyung ParkThe University of Texas at AustinDirección de correo verificada de utexas.edu
Praneeth NetrapalliGoogle ResearchDirección de correo verificada de google.com
Amir KalevInformation Sciences Institute, University of Southern CaliforniaDirección de correo verificada de isi.edu
Nicolas BoumalEcole Polytechnique Fédérale de Lausanne (EPFL)Dirección de correo verificada de epfl.ch
Alexandros G DimakisProfessor, ECE University of Texas at AustinDirección de correo verificada de austin.utexas.edu
David McAllesterProfessor, Toyota Technological Institute at ChicagoDirección de correo verificada de ttic.edu

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Srinadh Bhojanapalli

Research Scientist, Google Research

Dirección de correo verificada de google.com - Página principal

Machine learning Algorithms


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in neural information processing systems 30, 2017	1427	2017
Large batch optimization for deep learning: Training bert in 76 minutes Y You, J Li, S Reddi, J Hseu, S Kumar, S Bhojanapalli, X Song, J Demmel, ... arXiv preprint arXiv:1904.00962, 2019	1022	2019
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks B Neyshabur, S Bhojanapalli, N Srebro arXiv preprint arXiv:1707.09564, 2017	671	2017
Towards understanding the role of over-parametrization in generalization of neural networks B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro arXiv preprint arXiv:1805.12076, 2018	606	2018
Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in neural information processing systems 30, 2017	542	2017
Global optimality of local search for low rank matrix recovery S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 3873-3881, 2016	454	2016
Understanding robustness of transformers for image classification S Bhojanapalli, A Chakrabarti, D Glasner, D Li, T Unterthiner, A Veit Proceedings of the IEEE/CVF international conference on computer vision …, 2021	431	2021
Does label smoothing mitigate label noise? M Lukasik, S Bhojanapalli, A Menon, S Kumar International Conference on Machine Learning, 6448-6458, 2020	379	2020
Are transformers universal approximators of sequence-to-sequence functions? C Yun, S Bhojanapalli, AS Rawat, SJ Reddi, S Kumar arXiv preprint arXiv:1912.10077, 2019	335	2019
Dropping convexity for faster semi-definite optimization S Bhojanapalli, A Kyrillidis, S Sanghavi Conference on Learning Theory, 530-582, 2016	185	2016
Coherent matrix completion. Y Chen, S Bhojanapalli, S Sanghavi, R Ward arXiv preprint arXiv:1306.2979, 2013	151	2013
Modifying memories in transformer models C Zhu, AS Rawat, M Zaheer, S Bhojanapalli, D Li, F Yu, S Kumar arXiv preprint arXiv:2012.00363, 2020	135	2020
Universal matrix completion S Bhojanapalli, P Jain International Conference on Machine Learning, 1881-1889, 2014	129	2014
Stabilizing GAN training with multiple random projections B Neyshabur, S Bhojanapalli, A Chakrabarti arXiv preprint arXiv:1705.07831, 2017	108	2017
Completing any low-rank matrix, provably Y Chen, S Bhojanapalli, S Sanghavi, R Ward The Journal of Machine Learning Research 16 (1), 2999-3034, 2015	97	2015
Low-rank bottleneck in multi-head attention models S Bhojanapalli, C Yun, AS Rawat, S Reddi, S Kumar International conference on machine learning, 864-873, 2020	83	2020
Coping with label shift via distributionally robust optimisation J Zhang, A Menon, A Veit, S Bhojanapalli, S Kumar, S Sra arXiv preprint arXiv:2010.12230, 2020	74	2020
O (n) connections are expressive enough: Universal approximability of sparse transformers C Yun, YW Chang, S Bhojanapalli, AS Rawat, S Reddi, S Kumar Advances in Neural Information Processing Systems 33, 13783-13794, 2020	71	2020
The lazy neuron phenomenon: On emergence of activation sparsity in transformers Z Li, C You, S Bhojanapalli, D Li, AS Rawat, SJ Reddi, K Ye, F Chern, ... arXiv preprint arXiv:2210.06313, 2022	62	2022
A simple and effective positional encoding for transformers PC Chen, H Tsai, S Bhojanapalli, HW Chung, YW Chang, CS Ferng arXiv preprint arXiv:2104.08698, 2021	61	2021

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