PPT-Constrained Conditional Models Tutorial

Jingyu Chen Xiao Cheng Introduction Main ideas Idea 1 Modeling Separate modeling and problem formulation from algorithms Similar to the philosophy of probabilistic modeling Idea . Fleet and Neil D Lawrence Massachusetts Institute of Technology Cambridge MA 02139 USA University of Toronto Canada M5S 3H5 School of Computer Science University of Manchester M13 9PL UK Abstract Learned activityspeci64257c motion models are useful However they have limited expressive power and often cannot represent the posterior distribution correctly While learning the parameters of such models which have insu64259cient expressivity researchers use loss functions to penalize certain misrepr Imaging Science and Biomedical Engineering University of Manchester Manchester M13 9PT UK davidcristinaccemanchesteracuk Abstract We present an ef64257cient and robust model matching method which uses a joint shape and texture appearance model to ge ROBERT ENGLE. DIRECTOR: VOLATILITY INSTITUTE AT NYU STERN. RECENT ADVANCES IN COMMODITY MARKETS. QUEEN MARY, NOV,8,2013. VOLATIliTY. AND ECONOMIC DECISIONS. Asset prices change over time as new information becomes available.. Khashabi. CS 546. UIUC, 2013. Conditional Random Fields . and beyond …. Outline. Modeling . Inference. Training. Applications. Outline. Modeling . Problem definition. Discriminative vs. Generative. Khashabi. CS 546. UIUC, 2013. Conditional Random Fields . and beyond …. Outline. Modeling . Inference. Training. Applications. Outline. Modeling . Problem definition. Discriminative vs. Generative. 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. A Practically Fast Solution for . an . NP-hard Problem. Xu. Sun (. 孫 栩. ). University of Tokyo. 2010.06.16. Latent dynamics workshop 2010. Outline. Introduction. Related Work & Motivations. Our proposals. with Overlapping Data Inference. Esben Hedegaard and Bob Hodrick. Arizona State Univ. Columbia and. . NBER . HPSG1. by . Sibel. . Ciddi. Major Focuses of Research in This Field:. Unification-Based Grammars. Probabilistic Approaches . Dynamic Programming . Stochastic Attribute-Value Grammars, Abney, 2007. Dynamic Programming for Parsing and Estimation of Stochastic Unification-Based Grammars, Geman & Johnson, 2002. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Generative vs. Discriminative models. Christopher Manning. Introduction. So far we’ve looked at “generative models”. Language models, Naive Bayes. But there is now much use of conditional or discriminative probabilistic models in NLP, Speech, IR (and ML generally). Human and Machine Learning. Mike . Mozer. Department of Computer Science and. Institute of Cognitive Science. University of Colorado at Boulder. Today’s Plan. Hand back Assignment 1. More fun stuff from motion perception model. * Figures are from the . textbook site. .. II. Naïve Bayes model. III. Revisiting the . wumpus. world. I. Combining Evidence. What happens when we have two or more pieces of evidences?. . Suppose we know the full joint distribution..