Monte Carlo Dropout

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11 papers0 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Bayesian classification of astronomical spectra with class uncertainties
Sep 18, 2026Simon Barton, Martin Sahlén, Andreas Korn +1Monte Carlo Dropout Multiclass Classification
Theoretical Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden. · Observational Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden · Division of High Energy Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden +1
Assessing Reliability of BERT-Based Models on Question Answering Tasks
Aug 11, 2026Pooja Yadav, Priyanka Harjule, Basant Agarwal +1Question Answering Monte Carlo Dropout
Department of Mathematics, Malaviya National Institute of Technology, Jaipur, Rajasthan, India · Department of Computer Science and Engineering, Central University of Rajasthan, Kishangarh, Rajasthan, India · University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia
Uncertainty quantification for trustworthy deep learning: Methods and measures
Jul 30, 2026H. Martin Gillis, Thomas TrappenbergBayesian Neural Networks Uncertainty Quantification
Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada
Bayesian uncertainty estimation improves clinical decision making in medical AI agents
Jul 22, 2026Frederik Hauke, Patrick Wienholt, Christiane Kuhl +4Clinical Decision Support Clinical Decision-Making
Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. · Department of Medical Oncology, National Center for Tumor Diseases (NCT), Heidelberg University Hospital, Heidelberg, Germany. · Else Kröner Fresenius Center for Digital Health, TU Dresden, Dresden, Germany. +1
Confidence is Not Reliability: Rethinking MC Dropout in Brain Tumour Segmentation
Jun 17, 2026Xin Ci Wong, Duygu Sarikaya, Kieran Zucker +2Monte Carlo Dropout Segmentation Uncertainty Estimation
Centre for Doctoral Training in AI for Medical Diagnosis and Care, School of Computing, University of Leeds · School of Computer Science, University of Leeds · Leeds Cancer Centre, St James’s University Hospital, Leeds, UK
Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
May 28, 2026Shadmehr Zaregarizi, Khashayar YavariUncertainty Quantification Time Series Forecasting
Politecnico di Torino · Turin, Italy
Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
May 22, 2026Rouaa Hoblos, Noura Dridi, Noureddine Zerhouni +1Evidential Deep Learning Uncertainty Quantification
FEMTO-ST · Universit´e Marie et Louis Pasteur, SUPMICROTECH, CNRS, Institut FEMTO-ST, F-25000 Besan¸con, France
Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
Apr 15, 2026Mohammad Nooraiepour, Zezhang Song, Wei Li +1Monte Carlo Dropout Physics-Informed ML
Environmental Geosciences, Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316 Oslo, Norway. · National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beĳing), China · College of Geosciences, China University of Petroleum (Beĳing), China +3
U-Cast: A Surprisingly Simple and Efficient Frontier Probabilistic AI Weather Forecaster
Apr 10, 2026Salva Rühling Cachay, Duncan Watson-Parris, Rose YuMonte Carlo Dropout Weather Forecasting
UC San Diego
A deep learning pipeline for PAM50 subtype classification using histopathology images and multi-objective patch selection
Apr 2, 2026Arezoo Borji, Gernot Kronreif, Bernhard Angermayr +6Monte Carlo Dropout Histopathology Image Classification
Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria · Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems an der Donau, Austria. · Department of Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria +3
Dropout Robustness and Cognitive Profiling of Transformer Models via Stochastic Inference
Mar 18, 2026Antônio Junior Alves Caiado, Michael HahslerMonte Carlo Dropout Language Model Robustness
Lyle School of Engineering, Southern Methodist University, Dallas, TX, USA

Sep 18, 2026J/K move · Enter open · S save

Bayesian classification of astronomical spectra with class uncertainties

Simon Barton, Martin Sahlén, Andreas Korn, Christian Glaser

Theoretical Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden. · Observational Astrophysics, Division of Astronomy and Space Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden · Division of High Energy Physics, Department of Physics and Astronomy, Uppsala University, Box 524, 751 20 Uppsala, Sweden+1

Monte Carlo Dropout Multiclass Classification Uncertainty Calibration Bayesian Neural Networks

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Context: We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming 4MOST survey. In fulfilment of the survey requirements, this method should be able to express uncertainty in the input data as well as uncertainty introduced in its prediction. Aims: Four different methods are explored: (1) convolutional neural networks (CNNs), (2) the Dirichlet distribution, (3) Monte Carlo dropout (MCD), (4) Bayesian neural Networks (BNNs) + variational inference (VI). Training and validation was performed using labelled spectra from the SDSS database and a custom 4MOST mock dataset. All the methods were compared in terms of the same metrics: accuracy, area under the curve (AUC), expected calibration error (ECE), Shannon entropy, negative log-likelihood (NLL), Brier score, training time, and inference time. Methods: A CNN with simple architecture and about 20,000 parameters was trained to achieve classification accuracies of 91.5% on SDSS data and 92.8% on 4MOST mock data. The direct Dirichlet prediction and VI models tested provide uncertainties on class membership probabilities, but they confuse classes more often. The MCD on a CNN is found to be the most suitable; it boosts the point-estimate accuracies to 92.6% and 93.9%, while still providing fast training and sufficiently fast inference. Compared to a standard CNN, the method additionally provides well-calibrated uncertainties at marginal extra cost.