Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to applications such as situation awareness in disaster zones and mapping the spread of diseases. Such applications depend on classifying the situation across a region of interest, which can be depicted as a spatial "heatmap". Annotating unstructured data using crowdsourcing or automated classifiers produces individual classifications at sparse locations that typically contain many errors. We propose a novel Bayesian approach that models the relevance, error rates and bias of each information source, enabling us to learn a spatial Gaussian Process classifier by aggregating data from multiple sources with varying reliability and relevance. Our method does not require gold-labelled data and can make predictions at any location in an area of interest given only sparse observations. We show empirically that our approach can handle noisy and biased data sources, and that simultaneously inferring reliability and transferring information between neighbouring reports leads to more accurate predictions. We demonstrate our method on two real-world problems from disaster response, showing how our approach reduces the amount of crowdsourced data required and can be used to generate valuable heatmap visualisations from SMS messages and satellite images.
Dynamic Risk Assessment for Autonomous Vehicles from Spatio-Temporal Probabilistic Occupancy Heatmaps
Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA（加州大学伯克利分校土木与环境工程系）；D3 Analytics Innovation, Allstate Insurance Company, Northbrook, IL, USA（Allstate保险公司D3 Analytics创新部门）；Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA（加州大学伯克利分校电气工程与计算机科学系）
Human Pose Estimation by Probabilistic Mixtures of Gaussian and Uniform Distributions
University of Málaga（马拉加大学）；Instituto de Investigación Biomédica de Málaga y Plataforma en Nanomedicina – IBIMA Plataforma BIONAND（马拉加生物医学与纳米医学平台研究所（IBIMA BIONAND平台））；ITIS Software（ITIS软件公司）
Transferring model structure in Bayesian transfer learning for Gaussian process regression
Institute of Information Theory and Automation (UTIA), Czech Academy of Sciences（捷克科学院信息理论与自动化研究所（UTIA））；Trinity College Dublin, the University of Dublin（都柏林大学圣三一学院）
Confidence Optimization for Probabilistic Encoding
School of Computer Science and Technology, Beijing Institute of Technology（计算机科学与技术学院，北京理工大学）；School of Automation, Beijing Institute of Technology（自动化学院，北京理工大学）
针对概率编码中高斯噪声扭曲点距离及 KL 方差正则化依赖不可靠先验的问题，提出模型无关的 CPE，通过置信度感知距离调整和 L2 方差约束提升 BERT 与 RoBERTa 的分类泛化性能。
Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction
Stellenbosch University（斯泰伦博斯大学）；The Nature Conservancy（大自然保护协会）；University of Cape Town（开普敦大学）；Norwegian Institute for Nature Research—NINA（挪威自然研究所—NINA）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；South African Environmental Observation Network（南非环境观测网络）
Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Knowledge Engineering Group, Faculty of Computer Science, Otto von Guericke University Magdeburg, Germany；Biomedical Magnetic Resonance, Faculty of Nature Sciences, Otto von Guericke University Magdeburg, Germany；Genomics Research Centre, Human Technopole, Milan, Italy；Institute for Medical Engineering, Faculty of Electrical Engineering；Information Technology, Otto von Guericke University Magdeburg, Germany；Digital Engineering Faculty, University of Potsdam, Germany；Machine Learning, Hasso-Plattner-Institute, Potsdam, Germany；Department of Biomedical Data Science, Stanford University, Stanford, CA, United States；Centre for Behavioural Brain Sciences, Magdeburg, Germany；German Centre for Neurodegenerative Disease, Magdeburg, Germany
