Catégorie
Apprentissage automatique
Publications associées (1 000)
Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance
Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu
Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...
Ieee-Inst Electrical Electronics Engineers Inc2024The neural correlates of topographical disorientation-a lesion analysis study
Olaf Blanke, Lukas Heydrich, Eva Blondiaux
Topographical disorientation refers to the selective inability to orient oneself in familiar surroundings. However, to date its neural correlates remain poorly understood. Here we use quantitative lesion analysis and a lesion network mapping approach in or ...
Hoboken2024Scalable constrained optimization
Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...
EPFL2024DiffAirfoil: An Efficient Novel Airfoil Sampler Based on Latent Space Diffusion Model for Aerodynamic Shape Optimization
Surrogate-based optimization is widely used for aerodynamic shape optimization, and its effectiveness depends on representative sampling of the design space. However, traditional sampling methods are hard-pressed to effectively sample high-dimensional desi ...
2024Hydrogen-induced tunable remanent polarization in a perovskite nickelate
Materials with field-tunable polarization are of broad interest to condensed matter sciences and solid-state device technologies. Here, using hydrogen (H) donor doping, we modify the room temperature metallic phase of a perovskite nickelate NdNiO3 into an ...
Nature Portfolio2024Glenohumeral joint force prediction with deep learning
Dominique Pioletti, Alexandre Terrier, Patrick Goetti, Philippe Büchler
Deep learning models (DLM) are efficient replacements for computationally intensive optimization techniques. Musculoskeletal models (MSM) typically involve resource-intensive optimization processes for determining joint and muscle forces. Consequently, DLM ...
London2024Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks
Alfio Quarteroni, Francesco Regazzoni, Stefano Pagani
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 ...
Nature Portfolio2024In-Sensor Passive Speech Classification with Phononic Metamaterials
Mitigating the energy requirements of artificial intelligence requires novel physical substrates for computation. Phononic metamaterials have vanishingly low power dissipation and hence are a prime candidate for green, always-on computers. However, their u ...
Weinheim2024Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN
Analysis of single-cell datasets generated from diverse organisms offers unprecedented opportunities to unravel fundamental evolutionary processes of conservation and diversification of cell types. However, interspecies genomic differences limit the joint ...
Berlin2024