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

The inductive bias of deep learning: Connecting weights and functions

Deep Learning Generalization with Limited and Noisy Labels

Communication-efficient distributed training of machine learning models

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

Masked Training of Neural Networks with Partial Gradients

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

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

Deep Learning Generalization with Limited and Noisy Labels

Understanding generalization and robustness in modern deep learning

On the Generalization of Stochastic Gradient Descent with Momentum

The inductive bias of deep learning: Connecting weights and functions

Communication-efficient distributed training of machine learning models

Masked Training of Neural Networks with Partial Gradients

MULTI-TASK CURRICULUM LEARNING FOR PARTIALLY LABELED DATA

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

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