Materials property prediction with uncertainty quantification: A benchmark study

D Varivoda, R Dong, SS Omee, J Hu - Applied Physics Reviews, 2023 - pubs.aip.org
Uncertainty quantification (UQ) has increasing importance in the building of robust high-
performance and generalizable materials property prediction models. It can also be used in …

Adaptive learning of effective dynamics for online modeling of complex systems

I Kičić, PR Vlachas, G Arampatzis… - Computer Methods in …, 2023 - Elsevier
Predictive simulations are essential for applications ranging from weather forecasting to
material design. The veracity of these simulations hinges on their capacity to capture the …

Body knowledge and uncertainty modeling for monocular 3d human body reconstruction

Y Zhang, H Wang, JO Kephart… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
While 3D body reconstruction methods have made remarkable progress recently, it remains
difficult to acquire the sufficiently accurate and numerous 3D supervisions required for …

Uncertainty Quantification in Machine Learning for Biosignal Applications--A Review

IP de Jong, AI Sburlea, M Valdenegro-Toro - arXiv preprint arXiv …, 2023 - arxiv.org
Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature
of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG) …

Learning model uncertainty as variance-minimizing instance weights

N Jain, K Shanmugam, P Shenoy - The Twelfth International …, 2024 - openreview.net
Predictive uncertainty--a model's self-awareness regarding its accuracy on an input--is key
for both building robust models via training interventions and for test-time applications such …

Benchmarking uncertainty disentanglement: Specialized uncertainties for specialized tasks

B Mucsányi, M Kirchhof, SJ Oh - arXiv preprint arXiv:2402.19460, 2024 - arxiv.org
Uncertainty quantification, once a singular task, has evolved into a spectrum of tasks,
including abstained prediction, out-of-distribution detection, and aleatoric uncertainty …

[HTML][HTML] Relative evaluation of probabilistic methods for spatio-temporal wind forecasting

LØ Bentsen, ND Warakagoda, R Stenbro… - Journal of Cleaner …, 2024 - Elsevier
Short-term wind power forecasting has become a de facto tool to better facilitate the
integration of such renewable energy resources into modern power grids. Instead of point …

Individuality in Swarm Robots with the Case Study of Kilobots: Noise, Bug, or Feature?

M Raoufi, P Romanczuk, H Hamann - ALIFE 2023: Ghost in the …, 2023 - direct.mit.edu
Inter-individual differences are studied in natural systems, such as fish, bees, and humans,
as they contribute to the complexity of both individual and collective behaviors. However …

Hyperbolic active learning for semantic segmentation under domain shift

L Franco, P Mandica, K Kallidromitis, D Guillory… - arXiv preprint arXiv …, 2023 - arxiv.org
We introduce a hyperbolic neural network approach to pixel-level active learning for
semantic segmentation. Analysis of the data statistics leads to a novel interpretation of the …

Reservoir interwell connectivity estimation from small datasets using a probabilistic data driven approach and uncertainty quantification

M Thiam, A Nakhaee - Geoenergy Science and Engineering, 2023 - Elsevier
Over the past decade, various artificial intelligence types have made significant progress in
petroleum reservoir modeling, from machine learning to deep learning. These data-driven …