We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data compression and an inference network for parameter estimation. The summary network encodes raw observations into a fixed-size vector of summary features, while the inference network generates samples of the approximate posterior distribution of the model parameters based on these summary features. The posterior samples are produced in a deep generative fashion by sampling from a latent Gaussian distribution and passing these samples through an invertible transformation. We construct this invertible transformation by sequentially alternating conditional invertible neural network and conditional neural spline flow layers. The summary and inference networks are trained simultaneously. We apply the proposed method to an inversion problem in groundwater hydrology to estimate the posterior distribution of the log-conductivity field conditioned on spatially sparse time-series observations of the system's hydraulic head responses.The conductivity field is represented with 706 degrees of freedom in the considered problem.The comparison with the likelihood-based iterative ensemble smoother PEST-IES method demonstrates that the proposed method accurately estimates the parameter posterior distribution and the observations' predictive posterior distribution at a fraction of the inference time of PEST-IES.
StAD: Stein Amortized Divergence for Fast Likelihoods with Diffusion and Flow
Institute of Astronomy（天文研究所）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, UK（剑桥大学卡文迪许实验室，剑桥，英国）；Research Computing Services, University of Cambridge, Cambridge, UK（剑桥大学研究计算服务）；The Oskar Klein Centre, Department of Physics, Stockholm University, Stockholm, SE（斯德哥尔摩大学物理系奥斯卡·克莱因中心）；Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK（剑桥大学物理系卡文迪许实验室）
Learning Nonlinear Factor Models with Unknown Monotone Links from Incomplete and Noisy Data
School of Computation, Information and Technology, Technical University of Munich, Germany（计算、信息与技术学院，慕尼黑技术大学，德国）；Department of Economics, Virginia Tech, USA（经济系，弗吉尼亚理工学院，美国）；Munich Institute of Robotics and Machine Intelligence（慕尼黑机器人与人工智能研究所）
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology（大数据安全湖北工程研究中心，信息科学与工程学院，华中科技大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（计算机科学与技术学院，华中科技大学）；Key Laboratory of Information Storage System Ministry of Education of China, Huazhong University of Science and Technology（信息存储系统教育部重点实验室，华中科技大学）；Department of Electrical and Computer Engineering, University of Florida（电气与计算机工程系，佛罗里达大学）
Robust Federated Learning with Confidence-Weighted Filtering and GAN-Based Completion under Noisy and Incomplete Data
Computer Engineering Department, İstanbul Commerce University（伊斯坦布尔商业大学计算机工程系）；Turkcell Teknoloji（Turkcell技术公司）；Grid Communications and Security Group, Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory（橡树岭国家实验室电网安全与能源基础设施部门）
针对联邦学习中的噪声标签、类别缺失与不均衡问题，提出结合自适应噪声清洗、条件 GAN 合成补全和鲁棒训练的隐私保护框架，并在 MNIST 与 Fashion-MNIST 上验证其性能提升。
Flexible Amortized Variational Inference in qBOLD MRI
University of Sussex（萨塞克斯大学）；Brighton and Sussex Medical School（布莱顿和萨塞克斯医学院）；St. Vincent’s University Hospital（圣文森特大学医院）；University of Nottingham（诺丁汉大学）；Cardiff University（卡迪夫大学）
Performance of Rank-One Tensor Approximation on Incomplete Data
Inria（法国国家信息与自动化研究所）；ENS de Lyon（里昂高等师范学院）；CNRS（法国国家科学研究中心）；Université Claude Bernard Lyon 1（里昂第一大学）；Université Grenoble Alpes（格勒诺布尔-阿尔卑斯大学）；Grenoble INP（格勒诺布尔理工学院）
Multi-modal Graph Fusion for Inductive Disease Classification in Incomplete Datasets
Technical University of Munich (TUM)（慕尼黑工业大学）；German Center for Vertigo and Balance Disorders (DSGZ), Ludwig-Maximilians-Universität (LMU)（路德维希-马克西米利安大学（LMU）德国眩晕与平衡障碍中心（DSGZ））；Johns Hopkins University（约翰斯·霍普金斯大学）
DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
College of Engineering, Peking University（北京大学工学院）；School of Environmental Science and Engineering, Southern University of Science and Technology（南方科技大学环境科学与工程学院）；Peng Cheng Laboratory（鹏城实验室）
The Incomplete Rosetta Stone Problem: Identifiability Results for Multi-View Nonlinear ICA
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Max Planck Institute for Biological Cybernetics（马克斯·普朗克生物控制论研究所）；University of Cambridge（剑桥大学）；Max Planck ETH Center for Learning Systems（马克斯·普朗克-苏黎世联邦理工学院学习系统中心）；ETH Zürich（苏黎世联邦理工学院）
PIFON-EPT: MR-Based Electrical Property Tomography Using Physics-Informed Fourier Networks
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Massachusetts Institute of Technology（麻省理工学院）；New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
ASLSL: Adaptive shared latent structure learning with incomplete multi-modal physiological data for multi-dimensional emotional feature selection
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China（信息科学与技术学院，北京理工大学，北京）；School of Artificial Intelligence, Anhui University, Beijing 100124, China（人工智能学院，安徽大学，北京）
