Publications associées à A note on a Lyapunov argument for stochastic gradient methods in the presence of noise

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Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Patrick Thiran, Negar Kiyavash, Saber Salehkaleybar

We study the performance of Stochastic Cubic Regularized Newton (SCRN) on a class of functions satisfying gradient dominance property with

1\le\alpha\le2

which holds in a wide range of applications in machine learning and signal processing. This conditio ...

NeurIPS2022

A note on a Lyapunov argument for stochastic gradient methods in the presence of noise

Graph Chatbot

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Understanding generalization and robustness in modern deep learning

Residual-based attention in physics-informed neural networks

On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Generalization with Limited and Noisy Labels

Fast Adversarial Training With Adaptive Step Size

Masked Training of Neural Networks with Partial Gradients

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Generalization Properties of NAS under Activation and Skip Connection Search

Deep Learning Generalization with Limited and Noisy Labels

On the Generalization of Stochastic Gradient Descent with Momentum

Understanding generalization and robustness in modern deep learning

Masked Training of Neural Networks with Partial Gradients

Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Function

Generalization Properties of NAS under Activation and Skip Connection Search

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs

Residual-based attention in physics-informed neural networks

Fast Adversarial Training With Adaptive Step Size