Publications related to Exploring the robust extrapolation of high-dimensional machine learning potentials

Related publications (27)

Automated Ensemble Multimodal Machine Learning for Healthcare

The application of machine learning in medicine and healthcare has led to the creation of numerous diagnostic and prognostic models. However, despite their success, current approaches generally issue predictions using data from a single modality. This stan ...

2025

Non-asymptotic performance of social machine learning under limited data

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Ping Hu

This paper studies the probability of error associated with the social machine learning framework, which involves an independent training phase followed by a cooperative decision-making phase over a graph. This framework addresses the problem of classifyin ...

2025

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

Devavrat Tomar

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

EPFL2025

Solving the Cold-Start Problem for the Edge: Clustering and Adaptive Deep Learning for Emotion Detection

José Angel Miranda Calero

Designing AI-based applications personalized to each user's behavior presents significant challenges due to the cold start problem and the impracticality of extensive individual data labeling. These challenges are further compounded when deploying such app ...

IEEE2025

Accelerating MoE Model Inference with Expert Sharding

André Loureiro Espírito Santo, Anne-Marie Kermarrec, Rafael Pereira Pires, Marinus Abraham de Vos, Milos Vujasinovic

Mixture of experts (MoE) models achieve state-of-the-art results in language modeling but suffer from inefficient hardware utilization due to imbalanced token routing and communication overhead. While prior work has focused on optimizing MoE training and d ...

Association for Computing Machinery2025

Towards Practical Homomorphic Aggregation in Byzantine-Resilient Distributed Learning

Rachid Guerraoui, John Stephan

The growing availability of distributed data has led to the increased use of machine learning (ML) algorithms in distributed topologies, where multiple nodes collaborate to train models under the coordination of a central server. However, distributed learn ...

Association for Computing Machinery, Inc2024

Optimal Inference in Contextual Stochastic Block Models

Lenka Zdeborová, Odilon Marie Cyprien Duranthon

The contextual stochastic block model (CSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the context of machine learning on graphs, the ...

Journal of Machine Learning Research Inc.2024

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

Sandro Carrara, Francesca Rodino, Lin Du

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

Pergamon-Elsevier Science Ltd2024

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

Jean-Philippe Thiran, Erick Jorge Canales Rodriguez, Thomas Yu

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

IEEE Computer Society2024

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib

Roberto Castello, Federico Amato

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET exon 14 skipping alterations. While edema is the most prevalent adverse event (AE) and a known class effect of MET inhibitors including tepotinib, there is st ...

2024

Cell density quantification of high resolution Nissl images of the juvenile rat brain

Csaba Zsolnai, Henry Markram, Daniel Keller, Olivier Burri, Rodrigo de Campos Perin, Ying Shi, Julie Meystre, João Luís Prado Vieira, Jean Nicolas Jacquemier

Copyright © 2024 Meystre, Jacquemier, Burri, Zsolnai, Frank, Vieira, Shi, Perin, Keller and Markram.Nissl histology underpins our understanding of brain anatomy and architecture. Despite its importance, no high-resolution datasets are currently available i ...

2024

LegalWriter: An Intelligent Writing Support System for Structured and Persuasive Legal Case Writing for Novice Law Students

Seyed Parsa Neshaei

Novice students in law courses or students who encounter legal education face the challenge of acquiring specialized and highly concept-oriented knowledge. Structured and persuasive writing combined with the necessary domain knowledge is challenging for ma ...

Association for Computing Machinery2024

Backpropagation-free Training of Analog AI Accelerators

Romain Christophe Rémy Fleury, Ali Momeni, Matthieu Francis Malléjac

Deep learning has achieved remarkable success in diverse fields in recent years. However, this growth presents significant challenges, particularly in terms of energy consumption during both training and inference phases. While there have been efforts to i ...

Institute of Electrical and Electronics Engineers Inc.2024

Multi-motif scaffolding for de novo pathogen antigen mimetics by deep generative learning

Karla Montserrat Castro Gilabert

Proteins are macromolecular machines central to virtually every fundamental biological function. Three-dimensional protein structures, determined by their amino acid composition, are directly linked to their function. Research into structure-function relat ...

EPFL2024

Neurobiological and metabolic basis of motivation and effort-based decision-making - A human 1H-MRS, fMRI and machine learning study

Arthur Barakat

Motivation is a multifaceted phenomenon that we explore within the framework of decision-making. Through this cognitive process, actions are directed towards specific goals by performing a trade-off between the cost and benefit of an action. The dorsomedia ...

EPFL2024

Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG

Yoeri Renier Johannes Poels

Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles ...

2024

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Daniel Gatica-Perez, Sina Sajadmanesh

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to p ...

Assoc Computing Machinery2024

Review: III-V infrared emitters on Si: fabrication concepts, device architectures and down-scaling with a focus on template-assisted selective epitaxy

Kirsten Emilie Moselund

The local integration of III-Vs on Si is relevant for a wide range of applications in electronics and photonics, since it combines a mature and established materials platform with desired physical properties such as a direct and tuneable bandgap and high m ...

IOP Publishing Ltd2023

Difference in Intestine Content of Caenorhabditis elegans When Fed on Non-Pathogenic or Pathogenic Bacteria

Martinus Adela Maria Gijs, Farzad Rezaeianaran

We investigated the bacterial food digestion and accumulation in wild-type adult Caenorhabditis elegans (C. elegans) worms that have fed on either non-pathogenic RFP-expressing Escherichia coli (E. coli) OP50 or pathogenic-RFP-expressing Pseudomonas aerugi ...

MDPI2023

Self-Aware Social Learning Over Graphs

Ali H. Sayed, Stefan Vlaski, Virginia Bordignon, Konstantinos Ntemos

In this paper we study the problem of social learning under multiple true hypotheses and self-interested agents that exchange information over a graph. In this setup, each agent receives data that might be generated from a different hypothesis (or state) t ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023