PPT-Variational Inference in

Bayesian Submodular Models Josip Djolonga joint work with Andreas Krause Motivation inference with higher order potentials MAP Computation Inference We provide a method for inference in such models. Gershman sjgershmprincetonedu Department of Psychology Princeton University Green Hall Princeton NJ 08540 USA Matthew D Ho64256man mdhoffmacsprincetonedu Department of Statistics Columbia University New York NY 10027 USA David M Blei bleics Blei Computer Science Department Princeton University chongwjpaisleyblei csprincetonedu Abstract The hierarchical Dirichlet process HDP is a Bayesian nonparametric model that can be used to model mixedmembership data with a poten tially in64257nite 1 A New Beginning 113 112 De nition of Bar Member 113 113 Variational Formulation 114 1131 The Total Potential Energy Functional 114 1132 Admissible Variations 116 1133 The Minimum Total Potential Energy Principle 116 1134 TPE Discretization 117 Titsias MTITSIAS AUEB GR Department of Informatics Athens University of Economics and Business Greece Miguel L azaroGredilla MIGUEL TSC UC ES Dpt Signal Processing Communications Universidad Carlos III de Madrid Spain Abstract We propose a simple a uclacuk Kenichi Kurihara Dept of Computer Science Tokyo Institute of Technology kuriharamicstitechacjp Max Welling ICS UC Irvine wellingicsuciedu Abstract A wide variety of Dirichletmultinomial topic models have found interesting ap plications in rec Wangcsoxacuk Department of Computer Science University of Oxford Oxford OX1 3QD United Kingdom PhilBlunsomcsoxacuk Abstract Approximate inference for Bayesian models is dominated by two approaches variational Bayesian inference and Markov Chain Monte dlitcutokyoacjp The University of Tokyo Hiroshi Nakagawa n3dlitcutokyoacjp The University of Tokyo Abstract We propose a novel interpretation of the collapsed variational Bayes inference with a zeroorder Taylor expansion approximation called CVB0 inf . Bayesian. . Inference. I:. Pattern . Recognition . and. Machine Learning. Chapter 10. Falk. . LIEDER . December. 2 2010.  . Structural. . Approximations. Statistical . Inference. Introduction. . CRF Inference Problem. CRF over variables: . CRF distribution:. MAP inference:. MPM (maximum posterior . marginals. ) inference:. Other notation. Unnormalized. distribution. Variational. distribution. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. EGU 2012, Vienna. Michail Vrettas. 1. , Dan Cornford. 1. , Manfred Opper. 2. 1. NCRG, Computer Science, Aston University, UK. 2. Technical University of Berlin, Germany. Why do data assimilation?. Aim of data assimilation is to estimate the posterior distribution of the state of a dynamical model (X) given observations (Y). . Autoencoders. Theory and Extensions. Xiao Yang. Deep learning Journal Club. March 29. Variational. Inference. Use a simple distribution to approximate a complex distribution. Variational. parameter:. Inference. Dave Moore, UC Berkeley. Advances in Approximate Bayesian Inference, NIPS 2016. Parameter Symmetries. . Model. Symmetry. Matrix factorization. Orthogonal. transforms. Variational. . a. Henning Lange, Mario . Bergés. , Zico Kolter. Variational Filtering. Statistical Inference. (Expectation Maximization, Variational Inference). Deep Learning. Dynamical Systems. Variational Filtering.