Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computationally efficient. However, standard inference models are restricted to direct mappings from data to approximate posterior estimates. The failure of these models to reach fully optimized approximate posterior estimates results in an amortization gap. We aim toward closing this gap by proposing iterative inference models, which learn to perform inference optimization through repeatedly encoding gradients. Our approach generalizes standard inference models in VAEs and provides insight into several empirical findings, including top-down inference techniques. We demonstrate the inference optimization capabilities of iterative inference models and show that they outperform standard inference models on several benchmark data sets of images and text.
University of Helsinki（赫尔辛基大学）；ELLIS Institute Finland, Aalto University（芬兰埃利斯研究所、阿尔托大学）；TU Dortmund University（多特蒙德技术大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Independent Scientist（独立研究者）
Amortized Simulation-Based Inference in Generalized Bayes via Neural Posterior Estimation
Department of Statistics, University of Oxford, Oxford, United Kingdom（英国牛津大学统计系）；Department of Statistics, University of Auckland, Auckland, New Zealand（新西兰奥克兰大学统计系）
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
Department of Mathematics and Statistics, Indian Institute of Technology Kanpur（数学与统计学系，印度理工学院坎浦尔分校）；Marshall School of Business, University of Southern California（马歇尔商学院，南加州大学）
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Tübingen AI Center（图宾根人工智能中心）；Machine Learning in Science, Excellence Cluster Machine Learning, University of Tübingen（图宾根大学机器学习卓越集群科学机器学习系）；Department Empirical Inference, Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所经验推断系）
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
LMU Munich（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Imperial College London（帝国理工学院）；University of Technology Nuremberg (UTN)（纽伦堡工业大学）；Helmholtz AI（亥姆霍兹人工智能中心）；Xyme
From Mice to Trains: Amortized Bayesian Inference on Graph Data
Department of Statistics, TU Dortmund University（统计系，多特蒙德技术大学）；School of Public Health, Imperial College London（公共卫生学院，伦敦帝国理工学院）；Department of Biology, University of Oxford（生物学系，牛津大学）；Department of Mechanical Engineering, Karlsruhe Institute of Technology（机械工程系，卡尔斯鲁厄理工学院）
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
Department of Mechanical Engineering, Stanford University（斯坦福大学机械工程系）；Institute for Computational and Mathematical Engineering, Stanford University（斯坦福大学计算与数学工程研究所）；Bioengineering, Stanford University（斯坦福大学生物工程系）；Pediatric Cardiology, Stanford University（斯坦福大学儿科心脏病学）；Department of Applied and Computational Mathematics and Statistics, University of Notre Dame（诺丁汉大学应用与计算数学与统计学系）
Uncertainty-Aware Surrogate-based Amortized Bayesian Inference for Computationally Expensive Models
Department of Stochastic Simulation and Safety Research for Hydrosystems（随机模拟与安全研究部门）；Cluster of Excellence SimTech（卓越中心SimTech）；University of Stuttgart（斯图加特大学）；Department of Statistics（统计学系）；TU Dortmund University（多特蒙德技术大学）
Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networks
Cluster of Excellence SimTech（SimTech卓越集群）；University of Stuttgart（斯图加特大学）；Heidelberg University（海德堡大学）；Cluster of Excellence STRUCTURES（STRUCTURES卓越集群）
MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories
Laboratoire de Physique de l’École normale supérieure（巴黎高等师范学院物理实验室）；CNRS（法国国家科学研究中心）；PSL University（巴黎文理研究大学）；Sorbonne Université（索邦大学）；Université Paris Cité（巴黎西岱大学）；Max Planck Institute for Dynamics and Self-organization（马克斯·普朗克动力学与自组织研究所）；University of Washington（华盛顿大学）；Fred Hutchinson Cancer Research Center（弗雷德·哈钦森癌症研究中心）
University of Tübingen（蒂宾根大学）；Tübingen AI Center（蒂宾根人工智能中心）；University of British Columbia（不列颠哥伦比亚大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
ProximalFM: Amortized Proximal Causal Inference under Hidden Confounding
University of Oxford（牛津大学）；Harvard University（哈佛大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Vector Institute（向量研究所）；University College London（伦敦大学学院）
Instance-Adaptive Parametrization for Amortized Variational Inference
Department of Electrical Engineering and Information Technology, University of Naples Federico II（那不勒斯费德里科二世大学电气工程与信息技术系）；Department of Information Engineering, Electrical Engineering, and Applied Mathematics, University of Salerno（萨勒诺大学信息工程、电气工程与应用数学系）
