Publications associées à Predicting chemical hazard across taxa through machine learning

Publications associées (28)

Deep Neural Networks: Large-Width Behavior and Generalization Bounds

During several decades, neural network architectures have undergone considerable evolution: from shallow to deep, from fully connected to structured (e.g. convolutional, recurrent, or residual), from unnormalized to normalized. The present thesis contribut ...

EPFL2025

Source-Free Domain-Invariant Performance Prediction

Mathieu Salzmann

Accurately estimating model performance poses a significant challenge, particularly in scenarios where the source and target domains follow different data distributions. Most existing performance prediction methods heavily rely on the source data in their ...

Springer Nature2025

Technology Legalization and Optimization for Adiabatic Quantum-Flux Parametron

Giovanni De Micheli, Alessandro Tempia Calvino

Adiabatic quantum-flux parametron (AQFP) is an energy-efficient superconducting technology. Before physical design can be performed, AQFP technology mapping involves not only mapping logic into supported gate types but also legalizing the circuit to fulfil ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Spatiotemporal dynamics and humanitarian crisis in Bol (Chad): Satellite imagery analysis

Jérôme Chenal

This study investigates the impact of protracted humanitarian crises on urban development in Bol, a city in Chad’s Lake region, with a focus on spatial transformations and planning implications. The purpose is to assess how displacement-driven urbanization ...

2025

Targeting protein-ligand neosurfaces with a generalizable deep learning tool

Joseph Giuseppe Schmidt, Maddalena Elia, Yangyang Miao, Evgenia Elizarova, Bruno Emanuel Ferreira De Sousa Correia, Sebastian Maerkl, Luc Reymond, Philippe Schwaller, Pablo Gainza Cirauqui, Sandrine Madeleine Suzanne Georgeon, Leo Scheller, Anthony Marchand, Martin Pacesa, Arne Schneuing, Stephen Michael Buckley, Rebecca Manuela Neeser

Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules2, 3, 4-5. Despite recent ad ...

NATURE PORTFOLIO2025

Reweighting simulated events using machine-learning techniques in the CMS experiment

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk, Paul Théodore Gabriel Pablo De Bryas Dexmiers d'Archiac

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a geant-based simulation of the detect ...

2025

Metacognition meets AI: Empowering reflective writing with large language models

Tanja Christina Käser Jacober, Paola Mejia Domenzain, Seyed Parsa Neshaei

Reflective writing is known as a useful method in learning sciences to improve the metacognitive skills of students. However, students struggle to structure their reflections properly, limiting the possible learning gains. Previous works in educational tec ...

Wiley2025

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Devis Tuia, Marc Conrad Russwurm, Thiên-Anh Claris Nguyen, Gaston Jean Lenczner

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

Elsevier Science Inc2024

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

Olga Fink, Hao Lu

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 ...

2024

One-Bit Byzantine-Tolerant Distributed Learning via Over-The-Air Computation

Yuning Jiang

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learnin ...

2024

Adversarial Training for Jamming-Robust Channel Estimation in OFDM Systems

Pascal Frossard, Javier Alejandro Maroto Morales

Orthogonal frequency-division multiplexing (OFDM) is widely used to mitigate inter-symbol interference (ISI) from multipath fading. However, the open nature of wireless OFDM systems makes them vulnerable to jamming attacks. In this context, pilot jamming i ...

2024

AdaNCA: Neural Cellular Automata as Adaptors for More Robust Vision Transformer

Sabine Süsstrunk, Tong Zhang, Yitao Xu

Vision Transformers (ViTs) demonstrate remarkable performance in image classification through visual-token interaction learning, particularly when equipped with local information via region attention or convolutions. Although such architectures improve the ...

2024

Real-time quantum dynamics of thermal states with neural thermofields

Giuseppe Carleo, Jannes Willy E. Nys, Zakari Denis

Solving the time-dependent quantum many-body Schrödinger equation is a challenging task, especially for states at a finite temperature, where the environment affects the dynamics. Most existing approximating methods are designed to represent static thermal ...

2024

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

Lenka Zdeborová, Florent Gérard Krzakala, 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

Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems

Colin Neil Jones, Melanie Nicole Zeilinger, Roland Schwan, Xuan Truong Nghiem

Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As opposed to ...

IEEE2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

Planned, taught, learnt: analysis of transversal skills through curriculum using portfolio

Helena Kovacs, Alexandra Corina Niculescu, Tamara Milosevic

There is an everlasting effort in education to successfully ensure that the intended learning objectives are clearly taught and effectively learnt at the end of the educational cycle. This has been especially difficult in teaching and learning complex sets ...

TU Dublin2023

Reduced order modeling of parametrized pulsatile blood flows: Hematocrit percentage and heart rate

Ricardo Reyes Sotomayor

This paper numerically evaluates the accuracy and performance of a stabilized finite element Reduced Order Modelling (ROM) approach that is designed to simulate pulsatile blood flows. The method is able to estimate fluid flow parametric solutions of intere ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Learning of physical systems: from inference to control

Babak Rahmani

To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured. The complexity of full measurements of the system scales with its size. When exposed to real-w ...

EPFL2022

Predictive Analysis of Errors During Robot-Mediated Gamified Training

Pierre Dillenbourg, Arzu Güneysu Özgür

This paper presents our approach to predicting future error-related events in a robot-mediated gamified phys- ical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable fe ...

IEEE2022