Publications associées à Deep Learning Theory Through the Lens of Diagonal Linear Networks

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Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Nicolas Henri Bernard Flammarion, Hristo Georgiev Papazov, Scott William Pesme

In this work, we investigate the effect of momentum on the optimisation trajectory of gradient descent. We leverage a continuous-time approach in the analysis of momentum gradient descent with step size

\gamma

and momentum parameter

\beta

that allows u ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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Chattez avec Graph Search

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Explainable Face Verification via Feature-Guided Gradient Backpropagation

Deep Learning Generalization with Limited and Noisy Labels

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Benign Overfitting in Deep Neural Networks under Lazy Training

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Transformer Models for Vision

An exact mapping from ReLU networks to spiking neural networks

Probabilistic methods for neural combinatorial optimization

Deep Learning Generalization with Limited and Noisy Labels

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Benign Overfitting in Deep Neural Networks under Lazy Training

Leveraging Continuous Time to Understand Momentum When Training Diagonal Linear Networks

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Probabilistic methods for neural combinatorial optimization

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

An exact mapping from ReLU networks to spiking neural networks

Transformer Models for Vision

Explainable Face Verification via Feature-Guided Gradient Backpropagation