Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.
A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping
Université Caen Normandie, ENSICAEN, CNRS, Normandie Univ, GREYC UMR 6072, F-14000 Caen, France Universit\'e Paris-Saclay, Universit\'e Paris Cit\'e, CEA, CNRS, AIM, 91191, Gif-sur-Yvette, France Institutes of Computer Science；Astrophysics, Foundation for Research
Uncertainty quantification in fine-tuned LLMs using LoRA ensembles
Stockholm University Department of Physics（斯德哥尔摩大学物理系）；Department of Mathematical Sciences（数学科学系）；Chalmers university of technology & University of Gothenburg（楚德勒斯技术大学及哥德堡大学）；VERSES Research Lab（VERSES研究实验室）
TAM-Chain: Multi-Scale Thyroid Cytology Classification via Absorbing Markov Chains and Shannon Entropy Uncertainty Quantification for False-Negative Suppression and Domain-Shift Adaptation
Faculty of Mathematics, Mechanics and Informatics, VNU University of Science（越南国立大学理学院数学、力学与信息学系）
Model-Agnostic Dynamic Feature Selection with Uncertainty Quantification
School of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；University of Essex（埃塞克斯大学）；Institute of Mathematics（数学研究所）；Slovak Academy of Sciences（斯洛伐克科学院）
AI enhanced data assimilation and uncertainty quantification applied to Geological Carbon Storage
Faculty of Civil Engineering；Geosciences, TU Delft, Stevinweg 1, 2628 CN Delft, Netherlands；Centrum Wiskunde \& Informatica, Science Park 123, 1098 XG Amsterdam, Netherlands；Mathematical Institute, Utrecht University, Utrecht, Netherlands；Department of Earth Science；Engineering, Imperial College London, London, United Kingdom；Department of Energy Resources Engineering, Stanford University, CA, USA
Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Dept. of Molecular Medicine and Surgery, Karolinska Institutet（卡罗林斯卡研究所分子医学与外科部）；Biomedical Imaging Center, Pontificia Universidad Católica de Chile（智利天主教大学生物医学成像中心）；Department of Electrical Engineering, School of Engineering, Pontificia Universidad Católica de Chile（智利天主教大学工程学院电气工程系）；Millennium Institute for Intelligent Healthcare Engineering (iHEALTH), Santiago, Chile（智利智能健康工程研究院（iHEALTH））；Department of Medical Physics, Institute of Physics, University of Greifswald（格里夫斯瓦尔德大学物理研究所医学物理系）；Department of Radiology, Northwestern University（西北大学放射科）；Department of Neurological Surgery, Northwestern University（西北大学神经外科部）；Department of Neurology, Northwestern University（西北大学神经科）
An Overview of Large Language Models for Statisticians
Stanford University（斯坦福大学）；New York University（纽约大学）；Meta FAIR；University of Pennsylvania（宾夕法尼亚大学）；UC Berkeley（加州大学伯克利分校）；INRIA（法国国家信息与自动化研究所）；Rutgers University（罗格斯大学）
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processes
Department of Systems & Information Engineering, University of Virginia（系统与信息工程系，弗吉尼亚大学）；Department of Industrial & Operations Engineering, University of Michigan（工业与运营工程系，密歇根大学）
Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty
Interdisciplinary Health Sciences Institute (IHSI), University of Illinois Urbana-Champaign（跨学科健康科学研究院（IHSI），伊利诺伊大学厄巴纳-香槟分校）；Acoustic Research Laboratory, National University of Singapore, Tropical Marine Science Institute（声学研究实验室，新加坡国立大学，热带海洋科学研究所）；Department of Electrical and Computer Engineering, Stony Brook University（电气与计算机工程系，石溪大学）
JANET: Joint Adaptive predictioN-region Estimation for Time-series
Hasso Plattner Institute（哈索·普拉特纳研究所）；University of California, Irvine（加州大学尔湾分校）；Royal Holloway, University of London（伦敦大学皇家霍洛威学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）
Self-Supervised Conformal Prediction with Equivariant Bootstrapping for Image Uncertainty Quantification
Mullard Space Science Laboratory, University College London（伦敦大学学院穆拉德空间科学实验室）；IRFU, CEA, Université Paris-Saclay（法国原子能和替代能源委员会IRFU，巴黎萨克雷大学）；Heriot-Watt University（赫瑞瓦特大学）；Maxwell Institute for Mathematical Sciences（麦克斯韦数学科学研究所）；The Alan Turing Institute（艾伦·图灵研究所）
