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Neural Network Formalization

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Neural Network Formalization

Graph Chatbot

Chattez avec Graph Search

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep Learning Generalization with Limited and Noisy Labels

Privacy-preserving federated neural network training and inference

Penalising the biases in norm regularisation enforces sparsity

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Supervised learning and inference of spiking neural networks with temporal coding

Leveraging Unlabeled Data to Track Memorization

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep Learning Generalization with Limited and Noisy Labels

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Privacy-preserving federated neural network training and inference

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Leveraging Unlabeled Data to Track Memorization

Supervised learning and inference of spiking neural networks with temporal coding

Stop Wasting my FLOPS: Improving the Efficiency of Deep Learning Models

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks.

Penalising the biases in norm regularisation enforces sparsity