A Perspective on Gaussian Processes for Earth Observation
Universitat de València（瓦伦西亚大学）；University of Oxford（牛津大学）；German Aerospace Center（德国航空航天中心）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）
Gustau Camps-Valls, Dino Sejdinovic, Jakob Runge, Markus Reichstein
备注：
1 figure
发表信息：
National Science Review, Volume 6, Issue 4, July 2019, Pages 616-618
Earth observation (EO) by airborne and satellite remote sensing and in-situ observations play a fundamental role in monitoring our planet. In the last decade, machine learning and Gaussian processes (GPs) in particular has attained outstanding results in the estimation of bio-geo-physical variables from the acquired images at local and global scales in a time-resolved manner. GPs provide not only accurate estimates but also principled uncertainty estimates for the predictions, can easily accommodate multimodal data coming from different sensors and from multitemporal acquisitions, allow the introduction of physical knowledge, and a formal treatment of uncertainty quantification and error propagation. Despite great advances in forward and inverse modelling, GP models still have to face important challenges that are revised in this perspective paper. GP models should evolve towards data-driven physics-aware models that respect signal characteristics, be consistent with elementary laws of physics, and move from pure regression to observational causal inference.
Spatially-Heterogeneous Causal Bayesian Networks for Seismic Multi-Hazard Estimation: A Variational Approach with Gaussian Processes and Normalizing Flows
Johns Hopkins University（约翰斯·霍普金斯大学）；Data Science and AI Institute, Johns Hopkins University（约翰斯·霍普金斯大学数据科学与人工智能学院）
Analysis of a Generalized Expectation-Maximization Algorithm for Gaussian Mixture Models: A Control Systems Perspective
Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute（雷德利尔理工学院电子工程与计算机系统系）；Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute（雷德利尔理工学院工业与系统工程系）
Sequential Inference for Gaussian Processes: A Signal Processing Perspective
Basis Research Institute（基础研究机构）；Department of Electrical and Computer Engineering, Stony Brook University（石溪大学电气与计算机工程系）；Data Sciences Directorate, Brookhaven National Laboratory（布鲁赫斯国家实验室数据科学总局）
Unraveling the Smoothness Properties of Diffusion Models: A Gaussian Mixture Perspective
The University of Hong Kong（香港大学）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；The Simons Institute for the Theory of Computing at the University of California, Berkeley（加州大学伯克利分校西蒙斯理论计算研究所）；University of Pennsylvania（宾夕法尼亚大学）
Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective
Center for Industrial Mathematics, University of Bremen（工业数学中心，不莱梅大学）；Department of Theoretical Physics and Applied Mathematics, University of Cambridge（理论物理与应用数学系，剑桥大学）
Response probability distribution estimation of expensive computer simulators: A Bayesian active learning perspective using Gaussian process regression
Perspectives on AI Architectures and Co-design for Earth System Predictability
Pacific Northwest National Laboratory（太平洋西北国家实验室）；National Nuclear Security Administration（国家核安全管理局）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Carnegie Mellon University（卡内基梅隆大学）；Georgia Institute of Technology（佐治亚理工学院）；Oak Ridge National Laboratory（橡树岭国家实验室）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Will Artificial Intelligence supersede Earth System and Climate Models?
Helmholtz Centre Potsdam, German Research Centre for Geosciences GFZ（亥姆霍兹波茨坦中心，德国地球科学研究中心GFZ）；Free University of Berlin（柏林自由大学）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；University of Exeter（埃克塞特大学）；Princeton University（普林斯顿大学）；NOAA/OAR Geophysical Fluid Dynamics Laboratory（美国国家海洋和大气管理局/海洋与大气研究办公室地球物理流体动力学实验室）；University of Washington（华盛顿大学）；Colorado State University（科罗拉多州立大学）；German Climate Computing Center DKRZ（德国气候计算中心DKRZ）；Helmholtz-Zentrum Geesthacht, Center for Material and Coastal Research HZG（亥姆霍兹盖斯特哈赫特中心，材料与海岸研究中心HZG）
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component Analysis
Carnegie Mellon University（卡内基梅隆大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；International Max Planck Research School for Intelligent Systems (IMPRS-IS)（马克斯·普朗克智能系统国际研究学校（IMPRS-IS））；European Laboratory for Learning and Intelligent Systems (ELLIS)（欧洲学习与智能系统实验室（ELLIS））
There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation
K. N. Toosi University of Technology（KN图西理工大学）；Institute of Advanced Research in Artificial Intelligence (IARAI)（人工智能高级研究院（IARAI））；Istanbul Technical University（伊斯坦布尔理工大学）；Manipal Academy of Higher Education（马尼帕尔高等教育学院）；German Aerospace Center (DLR)（德国航空航天中心（DLR））；University of the Bundeswehr Munich（慕尼黑联邦国防军大学）
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运筹学研究中心）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
Conditional Diffusion-Based Retrieval of Atmospheric CO2 from Earth Observing Spectroscopy
University of Oklahoma（俄克拉荷马大学）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）；LumenUs Scientific（LumenUs科学公司）；Cooperative Institute for Atmospheric Research（大气研究合作研究所）；Colorado State University（科罗拉多州立大学）
