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Adaptive hot water production based on Supervised Learning

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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

Adaptive hot water production based on Supervised Learning

Graph Chatbot

Chattez avec Graph Search

Random matrix methods for high-dimensional machine learning models

Topics in statistical physics of high-dimensional machine learning

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

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

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Robust machine learning for neuroscientific inference

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

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Random matrix methods for high-dimensional machine learning models

Topics in statistical physics of high-dimensional machine learning

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

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

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Robust machine learning for neuroscientific inference

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

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Deep Learning Theory Through the Lens of Diagonal Linear Networks