受强制性开放获取政策约束的文章 - Spencer Frei了解详情
可在其他位置公开访问的文章：14 篇
Trained Transformers Learn Linear Models In-Context
R Zhang, S Frei, PL Bartlett
Journal of Machine Learning Research 25 (49), 2024
强制性开放获取政策： US National Science Foundation
Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data
S Frei, NS Chatterji, PL Bartlett
Conference on Learning Theory (COLT), 2022
强制性开放获取政策： US National Science Foundation
Agnostic Learning of a Single Neuron with Gradient Descent
S Frei, Y Cao, Q Gu
Advances in Neural Information Processing Systems (NeurIPS), 2020
强制性开放获取政策： US National Science Foundation
Algorithm-dependent generalization bounds for overparameterized deep residual networks
S Frei, Y Cao, Q Gu
Advances in Neural Information Processing Systems (NeurIPS), 2019
强制性开放获取政策： US National Science Foundation
Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization
S Frei, G Vardi, PL Bartlett, N Srebro
Conference on Learning Theory (COLT), 2023
强制性开放获取政策： US National Science Foundation
Random Feature Amplification: Feature Learning and Generalization in Neural Networks
S Frei, NS Chatterji, PL Bartlett
Journal of Machine Learning Research 24 (303), 2023
强制性开放获取政策： US National Science Foundation
Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent
S Frei, Q Gu
Advances in Neural Information Processing Systems (NeurIPS), 2021
强制性开放获取政策： US National Science Foundation
Provable Generalization of SGD-trained Neural Networks of Any Width in the Presence of Adversarial Label Noise
S Frei, Y Cao, Q Gu
International Conference on Machine Learning (ICML), 2021
强制性开放获取政策： US National Science Foundation
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks
S Frei, G Vardi, PL Bartlett, N Srebro
Advances in Neural Information Processing Systems (NeurIPS), 2023
强制性开放获取政策： US National Science Foundation
Self-training converts weak learners to strong learners in mixture models
S Frei, D Zou, Z Chen, Q Gu
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
强制性开放获取政策： US National Science Foundation
Agnostic Learning of Halfspaces with Gradient Descent via Soft Margins
S Frei, Y Cao, Q Gu
International Conference on Machine Learning (ICML), 2021
强制性开放获取政策： US National Science Foundation
Provable Robustness of Adversarial Training for Learning Halfspaces with Noise
D Zou, S Frei, Q Gu
International Conference on Machine Learning (ICML), 2021
强制性开放获取政策： US National Science Foundation
A lower bound for in range- bond percolation in two and three dimensions
S Frei, E Perkins
Electronic Journal of Probability 21, 2016
强制性开放获取政策： Natural Sciences and Engineering Research Council of Canada
Hemodynamic latency is associated with reduced intelligence across the lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and cognitive ability.
AE Anderson, M Diaz‑Santos, S Frei, BH Dang, P Kaur, P Lyden, ...
Brain Structure & Function, 2020
强制性开放获取政策： US National Institutes of Health
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