A unified stochastic approximation framework for learning in games

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A journey toward generalizable trajectory forecasting models

Mohammadhossein Bahari

Autonomous driving is a revolutionary technology that has seen considerable advancements through the adoption of deep learning solutions. One of the major challenges in this field is the interaction with other road users. This interaction necessitates a "t ...

EPFL2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Data-driven models in fusion exhaust: AI methods and perspectives

Yoeri Renier Johannes Poels

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

IOP Publishing Ltd2024

Scalable semantic 3D mapping of coral reefs with deep learning

Anders Meibom, Devis Tuia, Guilhem Maurice Louis Banc-Prandi, Jonathan Paul Sauder

Coral reefs are among the most diverse ecosystems on our planet, and essential to the livelihood of hundreds of millions of people who depend on them for food security, income from tourism and coastal protection. Unfortunately, most coral reefs are existen ...

2024

Learning to Boost the Performance of Stable Nonlinear Systems

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti

The growing scale and complexity of safety-critical control systems underscore the need to evolve current control architectures aiming for the unparalleled performances achievable through state-of-the-art optimization and machine learning algorithms. Howev ...

2024

Prediction rigidities for data-driven chemistry

Michele Ceriotti, Federico Grasselli, Philip Robin Loche, Matthias Linus Kellner, Sanggyu Chong, Filippo Bigi

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

2024

Nissl_3, Raw images for Machine learning for histological annotation and quantification of cortical layers

Julie Meystre

This dataset contains images (TIFF image data) of brain juvenile rats Wistar Han (P14) scanned immunostained slides by using an automated slide scanner (Olympus, VS120-L100, GER) equipped with a UPLSAPO 20x/0.75 air objective (Olympus, GER) and a Pike F5 ...

Zenodo2024

Beyond Essentials: Nuanced and Diverse Text-to-video Retrieval

Yuchen Yang

The field of text-to-video retrieval has advanced significantly with the evolution of language models and large-scale pre-training on generated caption-video pairs. Current methods predominantly focus on visual and event-based details, making retrieval lar ...

Institute of Electrical and Electronics Engineers Inc.2024

Surface segregation in high-entropy alloys from alchemical machine learning

Michele Ceriotti, Sandip De, Guillaume André Jean Fraux, Arslan Mazitov, Nataliya Lopanitsyna

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterog ...

Iop Publishing Ltd2024

Distributed Adaptive Learning Under Communication Constraints

Ali H. Sayed

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

Ieee-Inst Electrical Electronics Engineers Inc2024