In this draft, which reports on work in progress, we 1) adapt the information bottleneck functional by replacing the compression term by class-conditional compression, 2) relax this functional using a variational bound related to class-conditional disentanglement, 3) consider this functional as a training objective for stochastic neural networks, and 4) show that the latent representations are learned such that they can be used in a naive Bayes classifier. We continue by suggesting a series of experiments along the lines of Nonlinear In-formation Bottleneck [Kolchinsky et al., 2018], Deep Variational Information Bottleneck [Alemi et al., 2017], and Information Dropout [Achille and Soatto, 2018]. We furthermore suggest a neural network where the decoder architecture is a parameterized naive Bayes decoder.
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Rensselaer Polytechnic Institute（伦斯勒理工学院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Swiss AI, EPFL（瑞士人工智能，洛桑联邦理工学院）；Google DeepMind（谷歌DeepMind）；University of Delaware（特拉华大学）
Bridging the Gap between Probabilistic and Deterministic Models: A Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural Network Model
KAIST（韩国科学技术院）；Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
Disentangled Representation Learning with Transmitted Information Bottleneck
State Grid Shaanxi Electric Power Company Ltd.（国家电网陕西省电力公司）；State Grid Corporation of China（国家电网有限公司）；University of Science and Technology of China（中国科学技术大学）
Amortising Inference and Meta-Learning Priors in Neural Networks
Helmholtz AI（海德堡人工智能研究所）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Technical University of Munich（慕尼黑技术大学）；University of Technology Nuremberg（纽伦堡技术大学）
Principled MAP estimation for inverse problems: bridging the gap between convergence and performance
ENS de Lyon（里昂高等师范学校）；CNRS（法国国家科学研究中心）；Université Claude Bernard Lyon 1（里昂第一大学）；Inria（法国国家信息与自动化研究所）；Univ. Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）；IMB（波尔多数学研究所）
Enhancing Uncertainty Estimation and Interpretability via Bayesian Non-negative Decision Layer
National Key Laboratory of Radar Signal Processing, Xidian University（雷达信号处理国家重点实验室，西安电子科技大学）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学院，西安交通大学）；McCombs School of Business, The University of Texas at Austin（商学院，德克萨斯大学奥斯汀分校）
Bridging Data Barriers among Participants: Assessing the Potential of Geoenergy through Federated Learning
Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究院，东方理工大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Jiao Tong University（上海交通大学）；Research Institute for Smart Energy, The Hong Kong Polytechnic University（香港理工大学智慧能源研究院）
Depth-induced NTK: Bridging Over-parameterized Neural Networks and Deep Neural Kernels
State Key Laboratory of Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学，中国）；School of Intelligent Science and Technology, Nanjing University, China（智能科学与技术学院，南京大学，中国）；Department of Data Science, City University of Hong Kong, Hong Kong（数据科学系，香港城市大学，香港）
Misspecification-robust amortised simulation-based inference using variational methods
Institute of Astronomy, University of Cambridge（天文研究所，剑桥大学）；Statistical Laboratory, University of Cambridge（统计实验室，剑桥大学）；Kavli Institute for Cosmology, University of Cambridge（宇宙学研究所，剑桥大学）；Institute of Astrophysics, FORTH（天体物理学研究所，福蒂斯研究所）
