Zhiyuan Li

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Sanjeev AroraProfessor of Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Wei HuAssistant Professor of Computer Science and Engineering, University of MichiganPatvirtintas el. paštas umich.edu
Simon Shaolei DuAssistant Professor, School of Computer Science and Engineering, University of WashingtonPatvirtintas el. paštas cs.washington.edu
Ruosong WangAssistant Professor, Peking UniversityPatvirtintas el. paštas pku.edu.cn
Kaifeng LyuPrinceton UniversityPatvirtintas el. paštas princeton.edu
Tengyu MAStanford UniversityPatvirtintas el. paštas stanford.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUPatvirtintas el. paštas cs.cmu.edu
Dingli YuMicrosoft ResearchPatvirtintas el. paštas microsoft.com
Srinadh BhojanapalliResearch Scientist, Google ResearchPatvirtintas el. paštas google.com
Hong LiuStanford UniversityPatvirtintas el. paštas stanford.edu
Tianhao WangResearch Assistant Professor, Toyota Technological Institute at ChicagoPatvirtintas el. paštas ttic.edu
Kaiyue WenPhd Student, Stanford UniversityPatvirtintas el. paštas stanford.edu
Yann LeCunChief AI Scientist at Facebook & JT Schwarz Professor at the Courant Institute, New York UniversityPatvirtintas el. paštas cs.nyu.edu
Behnam NeyshaburMember of Technical Staff, AnthropicPatvirtintas el. paštas anthropic.com
Nathan SrebroProfessor, TTIC and University of ChicagoPatvirtintas el. paštas ttic.edu
Jason D. LeeAssociate Professor of Electrical Engineering and Computer Science, Princeton UniversityPatvirtintas el. paštas princeton.edu
Yi ZhangSenior Researcher at Microsoft Research RedmondPatvirtintas el. paštas microsoft.com
Percy LiangAssociate Professor of Computer Science, Stanford UniversityPatvirtintas el. paštas cs.stanford.edu
David HallStanfordPatvirtintas el. paštas stanford.edu
Sadhika MalladiPrinceton UniversityPatvirtintas el. paštas princeton.edu

Stebėti

Zhiyuan Li

Assistant Professor, Toyota Technological Institute at Chicago

Patvirtintas el. paštas ttic.edu - Pagrindinis puslapis

deep learning theory machine learning theory


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks S Arora, S Du, W Hu, Z Li, R Wang International conference on machine learning, 322-332, 2019	1147	2019
On exact computation with an infinitely wide neural net S Arora, SS Du, W Hu, Z Li, RR Salakhutdinov, R Wang Advances in neural information processing systems 32, 2019	1075	2019
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	651	2018
An exponential learning rate schedule for deep learning Z Li, S Arora arXiv preprint arXiv:1910.07454, 2019	251	2019
Harnessing the power of infinitely wide deep nets on small-data tasks S Arora, SS Du, Z Li, R Salakhutdinov, R Wang, D Yu	207	2019
Towards resolving the implicit bias of gradient descent for matrix factorization: Greedy low-rank learning Z Li, Y Luo, K Lyu arXiv preprint arXiv:2012.09839, 2020	151	2020
Enhanced convolutional neural tangent kernels Z Li, R Wang, D Yu, SS Du, W Hu, R Salakhutdinov, S Arora arXiv preprint arXiv:1911.00809, 2019	145	2019
Sophia: A scalable stochastic second-order optimizer for language model pre-training H Liu, Z Li, D Hall, P Liang, T Ma arXiv preprint arXiv:2305.14342, 2023	139	2023
Theoretical analysis of auto rate-tuning by batch normalization S Arora, Z Li, K Lyu arXiv preprint arXiv:1812.03981, 2018	133	2018
Learning in games: Robustness of fast convergence DJ Foster, Z Li, T Lykouris, K Sridharan, E Tardos Advances in Neural Information Processing Systems 29, 2016	131	2016
Understanding gradient descent on the edge of stability in deep learning S Arora, Z Li, A Panigrahi International Conference on Machine Learning, 948-1024, 2022	120	2022
What Happens after SGD Reaches Zero Loss?--A Mathematical Framework Z Li, T Wang, S Arora arXiv preprint arXiv:2110.06914, 2021	113	2021
Simple and effective regularization methods for training on noisily labeled data with generalization guarantee W Hu, Z Li, D Yu International Conference on Learning Representations (ICLR 2020), 2019	102*	2019
On the validity of modeling sgd with stochastic differential equations (sdes) Z Li, S Malladi, S Arora Advances in Neural Information Processing Systems 34, 12712-12725, 2021	100	2021
Explaining landscape connectivity of low-cost solutions for multilayer nets R Kuditipudi, X Wang, H Lee, Y Zhang, Z Li, W Hu, R Ge, S Arora Advances in neural information processing systems 32, 2019	99	2019
Gradient descent on two-layer nets: Margin maximization and simplicity bias K Lyu, Z Li, R Wang, S Arora Advances in Neural Information Processing Systems 34, 12978-12991, 2021	95	2021
Chain of thought empowers transformers to solve inherently serial problems Z Li, H Liu, D Zhou, T Ma arXiv preprint arXiv:2402.12875 1, 2024	91	2024
How Does Sharpness-Aware Minimization Minimizes Sharpness? K Wen, T Ma, Z Li The Eleventh International Conference on Learning Representations, 2023	88	2023
Understanding the generalization benefit of normalization layers: Sharpness reduction K Lyu, Z Li, S Arora Advances in Neural Information Processing Systems 35, 34689-34708, 2022	84	2022
Reconciling modern deep learning with traditional optimization analyses: The intrinsic learning rate Z Li, K Lyu, S Arora Advances in Neural Information Processing Systems 33, 14544-14555, 2020	81	2020

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