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COLA: Decentralized Linear Learning

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In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

2023

Frugal Decentralized Learning

Anne-Marie Kermarrec

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

IEEE2022

Lifelong Machine Learning with Data Efficiency and Knowledge Retention

Fei Mi

Artificial intelligence (AI) and machine learning (ML) have become de facto tools in many real-life applications to offer a wide range of benefits for individuals and our society. A classic ML model is typically trained with a large-scale static dataset in ...

EPFL2021

The role of convolutional neural networks in scanning probe microscopy: a review

Georg Fantner

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials desi ...

BEILSTEIN-INSTITUT2021

Physics-Inspired Structural Representations for Molecules and Materials

Michele Ceriotti, Félix Benedito Clément Musil, Andrea Grisafi

The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its properties, involves transforming the Cartesian coordinates of the atom ...

AMER CHEMICAL SOC2021

Landscape and training regimes in deep learning

Matthieu Wyart, Mario Geiger, Leonardo Petrini

Deep learning algorithms are responsible for a technological revolution in a variety oftasks including image recognition or Go playing. Yet, why they work is not understood.Ultimately, they manage to classify data lying in high dimension – a feat generical ...

2021

Neural controlled differential equations for crop classification

Accurate and scalable crop classification is important for food security and sustainable resources management. The temporal development of crops, i.e., their phenology, is a continuous phenomena that if properly captured, can help to discern them. The nove ...

2021

Impact of quantum-chemical metrics on the machine learning prediction of electron density

Ksenia Briling, Alberto Fabrizio

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...

AMER INST PHYSICS2021

Analysing the effects of residential mobility behaviours on the composition of personal network in Switzerland

Vincent Kaufmann, Guillaume Simon Joseph Drevon, Gil Viry

Individual life courses are marked by residential mobility often associated with family and workplace changes and therefore likely to be related to the types of personal relationships people develop and maintain. Evidence about the relationship between res ...

2021