Scalable Probabilistic Models for Face and Speaker Recognition

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In the classical NP-hard (metric) 𝑘-median problem, we are given a set of 𝑛 clients and centers with metric distances between them, along with an integer parameter 𝑘 ≥ 1. The objective is to select a subset of 𝑘 open centers that minimizes the total distan ...

Aircraft-based monitoring of wildlife is a popular way among conservation practitioners to obtain animal population counts over large areas. Nowadays, these aerial censuses are becoming increasingly scalable due to the advent of drone technology, which is ...

The generation of synthetic patient data that reflect the statistical properties of real data plays a fundamental role in today’s world because of its potential to (i) be enable proprietary data access for statistical and research purposes and (ii) increas ...

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 ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

Symmetry is one of the most central concepts in physics, and it is no surprise that it has also been widely adopted as an inductive bias for machine-learning models applied to the physical sciences. This is especially true for models targeting the properti ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The present invention relates to systems and methods for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject based on reports of Minor Hallucination (MH) events in combination with electrophysiological data of the s ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Coffee production serves as an important source of income for millions of farmers across many tropical countries. Given the scale of the production, accurate and up-to-date maps of coffee plantations are needed to detect, monitor, and mitigate potential ne ...

Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

The advancement of face recognition technology has delivered substantial societal advantages. However, it has also raised global privacy concerns due to the ubiquitous collection and potential misuse of individuals' facial data. This presents a notable par ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

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 ...

Humans are chronically exposed to airborne microplastics (MPs) by inhalation. Various types of polymer particles have been detected in lung samples, which could pose a threat to human health. Inhalation toxicological studies are crucial for assessing the e ...

In a collaboration between Ecole Polytechnique Fédérale de Lausanne (EPFL) and CEA, in the fall of 2020, the experimental Programme d’Étude en Transmission de l’Acier Lourd et ses Eléments (PETALE) was successfully carried out in the CROCUS reactor of EPFL ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Scalable Probabilistic Models for Face and Speaker Recognition

Chattez avec Graph Search

A (2+ε)-Approximation Algorithm for Metric 𝑘-Median

How to minimize the annotation effort in aerial wildlife surveys

Generative models for synthetic data generation: application to pharmacokinetic/pharmacodynamic data

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Probing the effects of broken symmetries in machine learning

Reliable data-driven decision-making through optimal transport

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

System for predicting a prognosis of the neuropsychological and/or neuropsychiatric status in a subject

Robust machine learning for neuroscientific inference

Boosting likelihood learning with event reweighting

Semantic Segmentation of Coffee Plantations from Sentinel-2 Time Series

Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature Compensation

Graph generative deep learning models with an application to circuit topologies

Random matrix methods for high-dimensional machine learning models

Inhalation of Microplastics—A Toxicological Complexity

High-Resolution Modeling Without Computation Slowdown for PETALE in CROCUS

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis