PPT-Variational

Radar Data Assimilation for 012 hour severe weather forecasting Juanzhen Sun National Center for Atmospheric Research Boulder Colorado sunjucaredu Outline Background Motivation . 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 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 Borwein Abstract Modern nonsmooth analysis is now roughly thirty64257ve years old In this paper I shall attempt to analyse brie64258y where the subject stands today where it should be going and what it will take to get there In summary the conclusio 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 . geometrically. Note that x . Now, since x is the unknown function to be found so as to minimize (or maximize) a functional, we want to see what happens to the functional () J x x is indicated . Bayesian. . Inference. I:. Pattern . Recognition . and. Machine Learning. Chapter 10. Falk. . LIEDER . December. 2 2010.  . Structural. . Approximations. Statistical . Inference. Introduction. 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. formulation for higher order macroscopic traffic. flow models of the GSOM family. J.P. . Lebacque. UPE-IFSTTAR-GRETTIA. Le Descartes 2, 2 rue de la Butte . Verte. F93166 Noisy-le-Grand, France. Jean-patrick.lebacque@ifsttar.fr. . CRF Inference Problem. CRF over variables: . CRF distribution:. MAP inference:. MPM (maximum posterior . marginals. ) inference:. Other notation. Unnormalized. distribution. Variational. distribution. 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:. Qifeng. Chen. Stanford University. Vladlen. . Koltun. Intel Labs. Optical flow. Motion field between two image frames. Optical flow. Motion field between two image frames. Image 1. Image 2. optical flow. Inference. Dave Moore, UC Berkeley. Advances in Approximate Bayesian Inference, NIPS 2016. Parameter Symmetries. . Model. Symmetry. Matrix factorization. Orthogonal. transforms. Variational. . a.