Uncertainty Quantification

Also known as UQ

Follow this topic Ask about this topic

Momentum

44 papers in the last four weeks, up 110% on the four weeks before. 0.4% of all new papers.

Jul 13Week of Sep 28

Latest papers 431

All topics

Fluctuations of Nonlinear Observables in Mean Field Neural Network Training
Oct 7, 2026Arnaud Descours, Geoffrey LacourNeural Network Training Dynamics Uncertainty Quantification
UCBL · MaIAGE
A Tale of Two Error Categories: Exploring Concealed Trade-Offs in the Errors of Automated Judges in Evaluation of Uncertainty Quantifiers
Oct 7, 2026Evgenia Ilia, Wilker AzizUncertainty Quantification Automated Evaluation
University of Amsterdam
SpatialUQ: Post-Hoc Uncertainty Quantification from Spatial Consistency in Black-Box Vision Models
Oct 7, 2026Md Kawsher Mahbub, Milon Biswas, Mirza Niaz Morshed +1Uncertainty Quantification Uncertainty Calibration
NPI University of Bangladesh · Towson University · University of Asia Pacific
Shape-Bayes: Bayesian Inference of Structured Shapes under Visual Ambiguity
Oct 6, 2026Mani Kumar Tellamekala, Tosh Brown, Michel ValstarBayesian Inference Uncertainty Quantification
Amsterdam, The Netherlands · BlueSkeye AI · University of Nottingham, U.K.
Uncertainty Quantification Is Indispensable for Reliable Connectome-Based Graph Learning: A Narrative Review and Case Study
Oct 6, 2026Mansooreh PakravanUncertainty Quantification Uncertainty Calibration
Department of Electrical and Computer Engineering, Tarbiat Modares University, Al Ahmad Street, Tehran 111-14115, Iran
Revar3r: gauge-aware perturbation uncertainty for feed-forward 3d reconstruction
Oct 6, 2026Sammam Mahdi, Fariha Binta Salim, Rakin Bin Rabbani +1Uncertainty Quantification 3D Reconstruction
BRAC University · Mahidol University
Targeted search shows that random-device testing underestimates worst-case error in a simulated wave-based neural operator
Oct 5, 2026Samrendra Roy, Jason Yoo, Souvik Chakraborty +1Fourier Neural Operator Uncertainty Quantification
Department of Nuclear, Plasma, and Radiological Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA · Department of Applied Mechanics, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India · Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India +1
Expressiveness, Equivalence, and Uncertainty in Velocity Obstacles and Closest Point of Approach Metrics
Oct 5, 2026Elizabeth Dietrich, Liam M. Imagawa, Hanna Krasowski +3Collision Avoidance Uncertainty Quantification
University of California, Berkeley, USA · Norwegian University of Science and Technology, Trondheim, Norway
Ensemble-Based Perceptual Audio Quality Assessment with Confidence Intervals
Oct 5, 2026Pablo M. Delgado, Andreas Brendel, Konstantin Schmidt +1Uncertainty Quantification
Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form
Oct 5, 2026Aleš Kučera, Karel ZimmermannUncertainty Quantification Safe Motion Planning
Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
A Statistical Inference Framework for PMI Estimation and SGNS Word Embeddings
Oct 4, 2026Zhongqi FanWord Embeddings Uncertainty Quantification
Beijing Normal-Hong Kong Baptist University
Auditing Routing Entropy as an Uncertainty Signal in Attention-Residual Transformers
Oct 1, 2026Wenhao Liang, Lin Yue, Wei Emma Zhang +3Uncertainty Quantification Model Calibration
Adelaide University, Adelaide, Australia
Localisation-Aware Uncertainty for Pretrained Object Detection
Oct 1, 2026Charmaine Barker, Daniel Bethell, Simos GerasimouEvidential Deep Learning Uncertainty Quantification
Department of Computer Science, University of York, UK · Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus
Reliability-aware short-term roll prediction for unmanned surface vehicles via multi-task learning and adaptive centralization
Oct 1, 2026Kaizhen Li, Xi Zhou, Zihao Wang +3Uncertainty Quantification Unmanned Surface Vehicle
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China · Shanghai Artificial Intelligence Laboratory, Shanghai, 200032, China · Institute of Artificial Intelligence, Shanghai University, Shanghai, 200444, China
Adaptive Conformal Prediction for Image Regression Models with Application to an Inertial Confinement Fusion Emulator
Sep 30, 2026Carrie J. Lei-Cramer, Michael S. Jones, Laura J. WendelbergerUncertainty Quantification Conformal Prediction
Department of Statistics, Texas A&M University, College Station, TX, USA. · Lawrence Livermore National Laboratory, Livermore, CA, USA.
Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification
Sep 30, 2026Gonzalo Esteban Mosquera Rojas, Sebastian R. van der Voort, Carolin M. Pirkl +3Uncertainty Quantification Brain Tumor Classification
Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands · Department of Medical Informatics, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands · GE HealthCare, Munich, Germany +3
Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
Sep 29, 2026Feiyang Li, Shengjing Liu, Qi Zhan +7Uncertainty Quantification Confidence Estimation in Language Models
College of Computer Science and Software Engineering, Shenzhen University · Tsinghua University · Behavioral and Spatial AI Lab, Peking University & Tongji University
Lucid Dreaming for World Models: Learning to Doubt Imagination and Decide by Trust
Sep 29, 2026Ziqi Wen, Ting Xu, Lianyu Wang +5Uncertainty Quantification World Model-Based Planning
Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore · Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore · Bioengineering Program, Biomedical Sciences Division (BioMed), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia +3
A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion
Sep 28, 2026Fred Xu, Thomas Markovich, Florence Regol +1Graph Neural Networks Uncertainty Quantification
Department of Computer Science University of California, Los Angeles · Block, Inc. · Mila, Quebec AI Institute
Multi-Task Learning of Conditional Mean Operators: applications to dynamical systems and uncertainty quantification
Sep 28, 2026Sami Chemlal, Thibaut Germain, Rémi Flamary +2Dynamical Systems Uncertainty Quantification
CMAP, Ecole Polytechnique · University of Novi Sad
Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound
Sep 28, 2026Camilla Ferrario, Maelys Venet, Olivier Villemain +1Parameter Estimation Uncertainty Quantification
CHU Bordeaux · EPIONE
Does Model Uncertainty Track Human Ambiguity? Evidence from Multi-Annotator Vision Benchmarks
Sep 28, 2026Manya Singh, Arjun PakrashiUncertainty Quantification
School of Computer Science, University College Dublin, Ireland
Probabilistic electrical power demand forecasting with uncertainty quantification
Sep 28, 2026Mahesh Neupane, Pragya Dhungana, Pradip Khatri +2Uncertainty Quantification Probabilistic Forecasting
Interconnection and Numbering Section Nepal Telecommunications Authority Kathmandu, Nepal · Nepal Electricity Authority Kathmandu, Nepal · Department of Global Public Health and Primary Care University of Bergen Bergen, Norway +1
Is your uncertainty map wrong, or is its target? Exact diagnostics for the Tweedie diagonal, and a gradient-free alternative
Sep 27, 2026Vicent Ribas, Anna Oliveras TousUncertainty Quantification Bayesian Inference
Eurecat · University of Barcelona
Optimal Transport Dropout for Structured Predictive Uncertainty
Sep 27, 2026Giacomo Lorenzon, Francesco RegazzoniUncertainty Quantification Uncertainty Calibration
MOX–Department of Mathematics, Politecnico di Milano, Italy
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Sep 25, 2026Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys +2Uncertainty Quantification Selective Prediction
Data Science Research Centre Tampere University Tampere, Finland
Residual Correlation as a Diagnostic for Joint-Uncertainty Gains from GP Coregionalisation
Sep 24, 2026Fangqin Zhou, Joaquin VanschorenUncertainty Quantification Multi-Task Learning
Eindhoven University of Technology
Direct Message Approximation (DMA): A Consistency-Based Framework for Tractable Approximate Inference on Factor Graphs
Sep 24, 2026Ralf Herbrich, Rainer Schlosser, Jan Lemcke +11Efficient Inference Uncertainty Quantification
Hasso Plattner Institute University of Potsdam Potsdam, Germany
Sharp Limits for Honest Uncertainty in Hard-Budget Repeated Evaluation
Sep 24, 2026Yezhou Cheng, Runjia Du, Zeming Liu +5Confidence Region Estimation Uncertainty Quantification
Independent · Northwestern University · Pinterest, Inc.
Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomography
Sep 24, 2026Alan Hsu, Jenna Samra, Alin Razvan Paraschiv +1Uncertainty Quantification 3D Reconstruction
Harvard University Cambridge, MA, USA · Center for Astrophysics | Harvard & Smithsonian Cambridge, MA, USA · National Solar Observatory Boulder, CO, USA

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Fluctuations of Nonlinear Observables in Mean Field Neural Network Training

Arnaud Descours, Geoffrey Lacour

UCBL · MaIAGE

Neural Network Training Dynamics Uncertainty Quantification Mean-Field Theory Shallow Neural Networks

Open paper AskPDF arXiv

Mean field limits describe the training dynamics of wide neural networks through the evolution of the empirical distribution of their parameters. Although functional central limit theorems characterize the asymptotic fluctuations of this distribution, quantities of practical interest are typically nonlinear observables of the parameter distribution rather than the distribution itself. In this work, we show how these mean field fluctuations propagate to finite dimensional nonlinear observables for shallow neural networks trained by stochastic gradient descent. Working in the weighted Sobolev space in which the limiting fluctuation process is constructed, we apply a functional Delta method under ordinary Fr{é}chet differentiability, without requiring Lions derivatives with respect to the measure variable. We obtain a central limit theorem for the observables and, under a suitable representation of their differentials, an explicit covariance formula inherited from the underlying mean field fluctuation theory. We also study whether prescribed quantities of interest can be recovered from the selected observations. Under a constant rank assumption, we prove that a quantity of interest factors locally through the observation functional if and only if, throughout a neighborhood, the kernel of the differential of the observation is contained in that of the quantity of interest. Thus, a differential condition expressed directly in the ambient Sobolev space yields an exact nonlinear local factorization. These results provide a framework both for quantifying finite-width uncertainty on observable, statistically or physically meaningful quantities and for assessing whether the chosen observations contain the information required to identify them.

Momentum

Related topics

Latest papers 431