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Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

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Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

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System and method for privacy-preserving distributed training of neural network models on distributed datasets

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System and method for privacy-preserving distributed training of neural network models on distributed datasets

ADAGRAD Avoids Saddle Points

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

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Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients

A unified framework for Hamiltonian deep neural networks

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