Publications related to Preconditioned Spectral Descent for Deep Learning

Deep Learning Theory Through the Lens of Diagonal Linear Networks

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

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EPFL2024

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

EPFL2024

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In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

EPFL2024

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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

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Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate scaled ResNet in the limit of infinitely deep and wide neural networks, of wh ...

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

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Oxford Univ Press2024

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Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

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Intraday solar irradiance forecasting using public cameras

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With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage ...

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Random matrix methods for high-dimensional machine learning models

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In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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Partial discharge localization in power transformer tanks using machine learning methods

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This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

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Preconditioned Spectral Descent for Deep Learning

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Understanding generalization and robustness in modern deep learning

Topics in statistical physics of high-dimensional machine learning

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Residual-based attention in physics-informed neural networks

Intraday solar irradiance forecasting using public cameras

Random matrix methods for high-dimensional machine learning models

Partial discharge localization in power transformer tanks using machine learning methods

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Understanding generalization and robustness in modern deep learning

Topics in statistical physics of high-dimensional machine learning

Residual-based attention in physics-informed neural networks

Generalization of Scaled Deep ResNets in the Mean-Field Regime

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

Intraday solar irradiance forecasting using public cameras

Random matrix methods for high-dimensional machine learning models

Partial discharge localization in power transformer tanks using machine learning methods