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Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

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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

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

Graph Chatbot

Chattez avec Graph Search

Robust machine learning for neuroscientific inference

Topics in statistical physics of high-dimensional machine learning

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

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Random matrix methods for high-dimensional machine learning models

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Incorporating Projective Geometry into Deep Learning

Topics in statistical physics of high-dimensional machine learning

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Random matrix methods for high-dimensional machine learning models

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Incorporating Projective Geometry into Deep Learning

Robust machine learning for neuroscientific inference

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

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks