Residual-based attention in physics-informed neural networks

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Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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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 ...

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Topics in statistical physics of high-dimensional machine learning

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High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

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Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

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The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

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High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Roya Moghaddasi Fereidani

EPFL2024

Robust machine learning for neuroscientific inference

Steffen Schneider

EPFL2024

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

Mattia Atzeni

EPFL2024