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Decision Learning and Adaptation Over Multi-Task Networks

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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

Decision Learning and Adaptation Over Multi-Task Networks

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Chat with Graph Search

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Reliable data-driven decision-making through optimal transport

Unveiling the complexity of learning and decision-making

Partial discharge localization in power transformer tanks using machine learning methods

Investigating latent behaviour in multiday activity scheduling

Optimal regimes for algorithm-assisted human decision-making

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Towards practical reinforcement learning for tokamak magnetic control

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Reliable data-driven decision-making through optimal transport

Unveiling the complexity of learning and decision-making

Partial discharge localization in power transformer tanks using machine learning methods

Investigating latent behaviour in multiday activity scheduling

Optimal regimes for algorithm-assisted human decision-making

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Towards practical reinforcement learning for tokamak magnetic control