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Knowledge Transfer with Jacobian Matching

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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

Knowledge Transfer with Jacobian Matching

Graph Chatbot

Chattez avec Graph Search

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Machine Learning for Modeling Stock Returns

Optimizing Dynamic Aperture Studies with Active Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

On the number of regions of piecewise linear neural networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Topics in statistical physics of high-dimensional machine learning

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

Gibbs sampling the posterior of neural networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Machine Learning for Modeling Stock Returns

Optimizing Dynamic Aperture Studies with Active Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

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

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Topics in statistical physics of high-dimensional machine learning

Global existence for perturbations of the 2D stochastic Navier-Stokes equations with space-time white noise

Gibbs sampling the posterior of neural networks

On the number of regions of piecewise linear neural networks