Publications associées à Stochastic Spectral Descent for Restricted Boltzmann Machines.

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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

Stochastic Spectral Descent for Restricted Boltzmann Machines.

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Understanding generalization and robustness in modern deep learning

On the Generalization of Stochastic Gradient Descent with Momentum

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Communication-efficient distributed training of machine learning models

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

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

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

On the Generalization of Stochastic Gradient Descent with Momentum

Deep Learning Generalization with Limited and Noisy Labels

Communication-efficient distributed training of machine learning models

Masked Training of Neural Networks with Partial Gradients

The inductive bias of deep learning: Connecting weights and functions

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

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

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Understanding generalization and robustness in modern deep learning

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