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Hamiltonian reconstruction as metric for variational studies

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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

Hamiltonian reconstruction as metric for variational studies

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Chattez avec Graph Search

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Sparse autoregressive neural networks for classical spin systems

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Topics in statistical physics of high-dimensional machine learning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

Sparse autoregressive neural networks for classical spin systems

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

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

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

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

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Topics in statistical physics of high-dimensional machine learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks