Publications associées à Multi-fidelity surrogate modeling using long short-term memory networks

Publications associées (24)

Efficiency of neural quantum states in light of the quantum geometric tensor

Neural quantum state (NQS) ans & auml;tze have shown promise in variational Monte Carlo algorithms by their theoretical capability of representing any quantum state. However, the reason behind the practical improvement in their performance with an increase ...

NATURE PORTFOLIO2025

Best practices for multi-fidelity Bayesian optimization in materials and molecular research

Jeremy Luterbacher, Philippe Schwaller

Springer Science and Business Media LLC2025

Machine learning-based prediction of fish acute mortality: implementation, interpretation, and regulatory relevance

Fernando Perez Cruz

Regulation of chemicals requires knowledge of their toxicological effects on a large number of species, which has traditionally been acquired through in vivo testing. The recent effort to find alternatives based on machine learning, however, has not focuse ...

2024

Exploring Masked Autoencoders for Sensor-Agnostic Image Retrieval in Remote Sensing

Gencer Sümbül

Self-supervised learning through masked autoencoders (MAEs) has recently attracted great attention for remote sensing (RS) image representation learning, and thus embodies a significant potential for content-based image retrieval (CBIR) from ever-growing R ...

2024

Assessment framework for deepfake detection in real-world situations

Touradj Ebrahimi, Yuhang Lu

Detecting digital face manipulation in images and video has attracted extensive attention due to the potential risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been employed and ...

2024

Revisiting Character-level Adversarial Attacks for Language Models

Volkan Cevher, Grigorios Chrysos, Fanghui Liu, Yongtao Wu, Elias Abad Rocamora

Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to invalid adv ...

2024

Deep Non-Rigid Structure-From-Motion: A Sequence-to-Sequence Translation Perspective

Tong Zhang

Directly regressing the non-rigid shape and camera pose from the individual 2D frame is ill-suited to the Non-Rigid Structure-from-Motion (NRSfM) problem. This frame-by-frame 3D reconstruction pipeline overlooks the inherent spatial-temporal nature of NRSf ...

2024

Leveraging Machine Learning for Advanced Nanoscale X-ray Analysis: Unmixing Multicomponent Signals and Enhancing Chemical Quantification

Cécile Hébert, Duncan Thomas Lindsay Alexander

Energy dispersive X-ray (EDX) spectroscopy in the transmission electron microscope is a key tool for nanomaterials analysis, providing a direct link between spatial and chemical information. However, using it for precisely determining chemical compositions ...

American Chemical Society (ACS)2024

Microservice Deployment for Satellite Edge AI Inference via Deep Reinforcement Learning

Yuning Jiang

Artificial intelligence (AI) is critical in evolving 5G and developing 6G networks, running on edge devices, and solving resource management challenges. The burgeoning number of edge devices draws attention to the potential of low-earth orbit (LEO) satelli ...

Institute of Electrical and Electronics Engineers Inc.2024

Generative AI-Enabled Conversational Interaction to Support Self-Directed Learning Experiences in Transversal Computational Thinking

Denis Gillet, Adrian Christian Holzer, Juan Carlos Farah, Abdessalam Ouaazki

As computational thinking (CT) becomes increasingly acknowledged as an important skill in education, self-directed learning (SDL) emerges as a key strategy for developing this capability. The advent of generative AI (GenAI) conversational agents has disrup ...

2024

Improving SAM Requires Rethinking its Optimization Formulation

Volkan Cevher, Kimon Antonakopoulos, Fabian Ricardo Latorre Gomez, Thomas Michaelsen Pethick, Wanyun Xie

This paper rethinks Sharpness-Aware Minimization (SAM), which is originally formulated as a zero-sum game where the weights of a network and a bounded perturbation try to minimize/maximize, respectively, the same differentiable loss. We argue that SAM shou ...

2024

Dynamic Voxels Based on Ego-Conditioned Prediction: An Integrated Spatio-Temporal Framework for Motion Planning

Alexandre Massoud Alahi

Prediction is a vital component of motion planning for autonomous vehicles (AVs). By reasoning about the possible behavior of other target agents, the ego vehicle (EV) can navigate safely, efficiently, and politely. However, most of the existing work overl ...

IEEE Institute of Electrical and Electronics Engineers2024

Probing and modulating inter-areal coupling in the cortical visual motion processing pathway with non-invasive brain stimulation

Michele Bevilacqua

In the last few years, stroke ranked as the second most common cause of death and is the third most significant condition affecting disability-adjusted life years (DALYs) worldwide. Being the most prevalent and quality of life impacting post-stroke symptom ...

EPFL2024

Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks

Rahul Parhi

We study the problem of estimating an unknown function from noisy data using shallow ReLU neural networks. The estimators we study minimize the sum of squared data-fitting errors plus a regularization term proportional to the squared Euclidean norm of the ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

Ariane Andrée Marie Delrocq, Sahand Jamal Rahi

Reading out neuronal activity from three-dimensional (3D) functional imaging requires segmenting and tracking individual neurons. This is challenging in behaving animals if the brain moves and deforms. The traditional approach is to train a convolutional n ...

Nature Publishing Group2023

Online interoperable resources for building hippocampal neuron models via the Hippocampus Hub

Felix Schürmann, Armando Romani

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

2023

The Facets of Intangible Heritage in Southern Chinese Martial Arts: Applying a Knowledge-Driven Cultural Contact Detection Approach

Yumeng Hou

Investigating the intangible nature of a cultural domain can take multiple forms, addressing for example the aesthetic, epistemic and social dimensions of its phenomenology. The context of Southern Chinese martial arts is of particular significance as it c ...

Association for Computing Machinery2023

Numerical study on the scale effect of tip vortex cavitation induced by incoming velocities and scale ratios

Mohamed Farhat

Large eddy simulations of tip vortex cavitation (TVC) around an elliptical hydrofoil is performed to study its scale effect. A satisfying agreement is obtained between the numerical and experimental data. It indicates that the scale effect of TVC is remark ...

PERGAMON-ELSEVIER SCIENCE LTD2023

Simulation of flow and heat transfer in a differentially heated cubical cavity using coarse Large Eddy Simulation

Konstantin Mikityuk

Two widely used sub-grid scale models namely: the standard and the dynamic Smagorinsky models were tested in a simulation of the flow in a thermally driven 3D cavity at turbulent Rayleigh number Ra = 10(9). The cubical cavity of 0.7 m side-length is set to ...

2023

Resource aware pricing for electric vehicle charging

Nils Gustav Nilsson

Electric vehicle charging facilities offer their capacity constrained electric charge and parking to users for a fee. As electric vehicle adoption grows, so too does the potential for excessive resource utilization. In this paper, we study how prices set b ...

PERGAMON-ELSEVIER SCIENCE LTD2023