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Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

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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

Short-term runoff forecasting in an alpine catchment with a long short-term memory neural network

Graph Chatbot

Chattez avec Graph Search

Machine Learning for Modeling Stock Returns

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Incorporating Projective Geometry into Deep Learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Network time series forecasting in photovoltaics power production

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

Random matrix methods for high-dimensional machine learning models

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

Incorporating Projective Geometry into Deep Learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Topics in statistical physics of high-dimensional machine learning

Random matrix methods for high-dimensional machine learning models

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

Machine Learning for Modeling Stock Returns

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Network time series forecasting in photovoltaics power production

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