'Generative model' presentation slideshows

Deep Learning for NL

1.15k views • 107 slides

Semantics

Semantics. and some syntax, math, and computational linguistics too. LING 001 - October 16, 2006 Joshua Tauberer. Semantics. Why does a sentence mean what it means? What are the meanings of words and how do they come together to make larger meanings (i.e. phrases, sentences)?

1.2k views • 72 slides

Voxel-Based Morphometry with Unified Segmentation

Voxel-Based Morphometry with Unified Segmentation. Ged Ridgway University College London. Thanks to: John Ashburner and the FIL Methods Group. Preprocessing in SPM. Realignment With non-linear unwarping for EPI fMRI Slice-time correction Coregistration Normalisation Segmentation

1.22k views • 66 slides

Expert Search

Expert Search. Data Sciences Summer Institute University of Illinois at Urbana-Champaign July 1, 2011. Expert Search Group. Expert Search is a search engine that returns a list of people who are experts in a particular area of study given a paper abstract or list of topics.

697 views • 60 slides

Probase : A Knowledge Base for Text Understanding

Probase : A Knowledge Base for Text Understanding. Integrating, representing, and reasoning over human knowledge: the grand challenge of the 21 st century. Knowledge Bases. Scope: Cross domain / Encyclopedia: (Freebase, Cyc , etc )

784 views • 43 slides

SPM5 Segmentation

SPM5 Segmentation. A Growing Trend. Larger and more complex models are being produced to explain brain imaging data. Bigger and better computers allow more powerful models to be used More experience among software developers Older and wiser

555 views • 35 slides

Non-Negative Matrix Factorization for Statistical Analysis Stan Young Paul Fogel, Doug Hawkins NISS MPC Vienna 11July 20

Non-Negative Matrix Factorization for Statistical Analysis Stan Young Paul Fogel, Doug Hawkins NISS MPC Vienna 11July 2007. Outline. Introduction Robust singular value decomposition Non-negative matrix factorization 4. Inference with NMF. Data Blocks (Zoo). 3-Way. PCA. Linear Regression.

596 views • 32 slides

Bayesian Probabilistic reasoning and learning

Bayesian Probabilistic reasoning and learning. Tang Ying State Key Lab of Cad&CG, Zhejiang Univeristy 03/10/04. Outline. Probability axioms The meaning of probability Forward probabilities and inverse probabilities Facial modeling example. Probability Axioms.

499 views • 32 slides

Active learning

Active learning . Haidong Shi, Nanyi Zeng Nov,12,2008. outline. 1, introduction 2, active learning with different methods 3, Employing EM and Pool-based Active Learning for Text Classification. introduction. 1, what is active learning?

913 views • 28 slides

Bayesian network inference

Bayesian network inference. Given: Query variables: X Evidence ( observed ) variables: E = e Unobserved variables: Y Goal: calculate some useful information about the query variables Posterior P( X | e ) MAP estimate arg max x P( x | e )

540 views • 27 slides

Robust Reading: Identification and Tracing of Ambiguous Names

Robust Reading: Identification and Tracing of Ambiguous Names. Xin Li, Paul Morie, Dan Roth University of Illinois at Urbana-Champaign.

396 views • 26 slides

Topic and Role Discovery In Social Networks

Topic and Role Discovery In Social Networks. Review of Topic Model. Review of Joint/Conditional Distributions. What do the following tell us: P( Z i ) P( Z i | {W,D}) P( Z i , Z j | {W,D}). Extending The Topic Model. Topic Model spawned gobs of research e.g., visual topic models

444 views • 26 slides

CS728 Lecture 5 Generative Graph Models and the Web

431 views • 25 slides

Why Generative Models Underperform Surface Heuristics

Why Generative Models Underperform Surface Heuristics. UC Berkeley Natural Language Processing John DeNero, Dan Gillick, James Zhang, and Dan Klein. cat ||| chat ||| 0.9 the cat ||| le chat ||| 0.8 dog ||| chien ||| 0.8 house ||| maison ||| 0.6 my house ||| ma maison ||| 0.9

407 views • 25 slides

Topic-Sentiment Mixture: Modeling Facets and Opinions in Weblogs

434 views • 25 slides

447 views • 21 slides

Bayes for beginners

Bayes for beginners. Claire Berna. Lieke de Boer. Methods for dummies 27 February 2013. Bayes rule Given marginal probabilities p(A ), p(B ), and the joint probability p(A,B ), we can write the conditional probabilities:. This is known as the product rule . .

417 views • 21 slides

Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network

Genetic-linkage mapping of complex hereditary disorders to a whole-genome molecular-interaction network. Ivan Iossifov, Tian Zheng, Miron Baron, T. Conrad Gilliam and Andrey Rzhetsky. Genome Research (2008) 18: 1150-1162. Common neurodevelopmental disorders:

339 views • 19 slides

Approaches of Digital design part 2

Approaches of Digital design part 2. Digital Architecture AE 461 course -2013-. Metamorphic design.

407 views • 17 slides

Image Warping using Empirical Bayes

Image Warping using Empirical Bayes. John Ashburner & Karl J. Friston. Wellcome Department of Cognitive Neurology, London, UK. Bayesian approaches to image warping.

248 views • 13 slides