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Sep 08, 2012

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1. On the Parameter Space of Generative Lexicalized Statistical Parsing Models Daniel M. Bikel University of Pennsylvania August 9th, 2004

2. August 9th, 2004 Daniel M. Bikel Slide 1

Inference in generative models of images and video

Inference in generative models of images and video. John Winn MSR Cambridge May 2004. Overview. Generative vs. conditional models Combined approach Inference in the flexible sprite model Extending the model. Generative vs. conditional models.

635 views • 40 slides

Seven Lectures on Statistical Parsing

Seven Lectures on Statistical Parsing. Christopher Manning LSA Linguistic Institute 2007 LSA 354 Lecture 1. Course logistics in brief. Instructor: Christopher Manning manning@cs.stanford.edu Office hours: Thu 10–12 Course time: Tu/Fr 8:00–9:45 [ugh!] 7 classes Work required for credit:

530 views • 42 slides

Maxent Models and Discriminative Estimation

Maxent Models and Discriminative Estimation. Generative vs. Discriminative models Christopher Manning. Introduction. So far we’ve looked at “generative models” Language models, Naive Bayes

721 views • 46 slides

Artificial Intelligence 1: Logic agents

Artificial Intelligence 1: Logic agents. “Thinking Rationally”. Computational models of human “thought” processes Computational models of human behavior Computational systems that “think” rationally Computational systems that behave rationally. Logical Agents.

510 views • 31 slides

Generative and Discriminative Models in Text Classification

Generative and Discriminative Models in Text Classification. David D. Lewis Independent Consultant Chicago, IL, USA Dave@DavidDLewis.com www.DavidDLewis.com Workshop on Challenges in Information Retrieval and Language Modeling, U Mass, CIIR, Amherst, MA, 11 Sept 2002 . Text Classification.

299 views • 11 slides

Ch. 17 Basic Statistical Models

Ch. 17 Basic Statistical Models. CIS 2033: Computational Probability and Statistics Prof. Longin Jan Latecki Prepared by: Nouf Albarakati. Basic Statistical Models. Random samples Statistical models Distribution features and sample statistics

319 views • 14 slides

Generative Models vs. Discriminative models

Generative Models vs. Discriminative models. Roughly: Discriminative Feedforw ard Bottom-up Generative Feedforward recurrent feedback Bottom-up horizontal top-down. Compositional generative models require a flexible, “universal,” representation format for relationships.

600 views • 33 slides

Constraining the progenitors of gamma-ray bursts

Constraining the progenitors of gamma-ray bursts. Andrew Levan University of Warwick. Parameter space. Kouveliotou et al. 1993. Parameter space. Kulkarni & Kasliwal 2010. Parameter space. SGRBs. LGRBs. ?. TDEs?. SGRs. LLGRBs. AGN.

350 views • 16 slides

LING / C SC 439/539 Statistical Natural Language Processing

LING / C SC 439/539 Statistical Natural Language Processing. Lecture 13 2/25/2013. Recommended reading. Jurafsky & Martin Chapter 4: N-gram language models and smoothing. Outline. Generative probabilistic models Language models More smoothing Evaluating language models

1.03k views • 75 slides

Statistical Natural Language Parsing

Statistical Natural Language Parsing. An exponential number of attachments. Attachment ambiguities. A key parsing decision is how we ‘ attach’ various constituents PPs , adverbial or participial phrases, infinitives, coordinations , etc . Catalan numbers: C n = (2 n )!/[( n +1)! n !]

184 views • 5 slides

Generative Models for Image Understanding

Generative Models for Image Understanding. Nebojsa Jojic and Thomas Huang Beckman Institute and ECE Dept. University of Illinois. Problem: Summarization of High Dimensional Data. Pattern Analysis: For several classes c=1,..,C of the data, define probability distribution functions p(x| c)

643 views • 56 slides

数理言語情報論　第 7 回

数理言語情報論　第 7 回. 2009 年 11 月 18 日. 数理言語情報学研究室　講師　二宮　崇. 今日の講義の予定. 前回までの復習 P CFG (Probabilistic Context Free Grammar, 確率付文脈自由文法 ) マルコフ文法論文 Michael Collins (1997) Three generative, lexicalised models for statistical parsing, In proc. of ACL-EACL, p.16—23

857 views • 55 slides

RE3/1 & RE4/1 Parameter Space Services

RE3/1 & RE4/1 Parameter Space Services. Ian Crotty 12 Feb 2014. CMS Parameter drawing. RE4/1 & RE3/1. E xtending to Extra H igh Eta = 2.4 or 2.5. As planned high Eta up to 2.1. Boundries on Extra High Eta. Cylindrical Neutron Shielding. Alignment lines. RE4/1. RE3/1. ETA=2.1.

289 views • 19 slides

Chunk Parsing

Chunk Parsing. Chunk Parsing. Also called chunking , light parsing , or partial parsing . Method: Assign some additional structure to input over tagging Used when full parsing not feasible or not desirable. Because of the expense of full-parsing, often treated as a stop-gap solution.

496 views • 22 slides

Statistical Modeling and Learning in Vision --- cortex-like generative models Ying Nian Wu

Statistical Modeling and Learning in Vision --- cortex-like generative models Ying Nian Wu UCLA Department of Statistics JSM, August 2010. Outline Primary visual cortex (V1) Modeling and learning in V1 Layered hierarchical models. http://www.stat.ucla.edu/~ywu/ActiveBasis

600 views • 43 slides

Statistical NLP Spring 2011

Statistical NLP Spring 2011. Lecture 17: Parsing III. Dan Klein – UC Berkeley. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A. Treebank PCFGs. [Charniak 96]. Use PCFGs for broad coverage parsing

615 views • 51 slides

人工知能特論 II

人工知能特論 II. 二宮　崇. 今日の講義の予定. P CFG (Probabilistic Context Free Grammar, 確率付文脈自由文法 ) マルコフ文法論文 Michael Collins (1997) Three generative, lexicalised models for statistical parsing, In proc. of ACL-EACL, p.16—23

680 views • 55 slides

Parsing

Parsing. SLP Chapter 13. Outline. Parsing with CFGs Bottom-up, top-down CKY parsing Mention of Earley and chart parsing. Parsing. Parsing with CFGs refers to the task of assigning trees to input strings Trees that covers all and only the elements of the input and has an S at the top

600 views • 27 slides

Seven Lectures on Statistical Parsing

Seven Lectures on Statistical Parsing. Christopher Manning LSA Linguistic Institute 2007 LSA 354 Lecture 2. Attendee information. Please put on a piece of paper: Name: Affiliation: “Status” (undergrad, grad, industry, prof, …): Ling/CS/Stats background:

570 views • 45 slides

Parameter tuning based on response surface models An update on work in progress

Parameter tuning based on response surface models An update on work in progress. EARG, Feb 27 th , 2008 Presenter: Frank Hutter. Motivation. Parameter tuning is important Recent approaches (ParamILS, racing, CALIBRA) “only” return the best parameter configuration

375 views • 24 slides

Generative Models

Generative Models. for probabilistic inference. Michael Stewart. Remember the Joint Distribution?. What about very large/complex models?. "Generative" Modeling. Implement probability theory in computer science to infer a joint distribution

319 views • 10 slides

Building of statistical models

Building of statistical models. Guido Gerig Department of Computer Science, UNC, Chapel Hill. Statistical Shape Models. Drive deformable model segmentation statistical geometric model statistical image boundary model

562 views • 38 slides

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Presentation Transcript

Inference in generative models of images and video

Seven Lectures on Statistical Parsing

Maxent Models and Discriminative Estimation

Artificial Intelligence 1: Logic agents

Generative and Discriminative Models in Text Classification

Ch. 17 Basic Statistical Models

Generative Models vs. Discriminative models

Constraining the progenitors of gamma-ray bursts

LING / C SC 439/539 Statistical Natural Language Processing

Statistical Natural Language Parsing

Generative Models for Image Understanding

数理 言語情報論 第 7 回

RE3/1 &amp; RE4/1 Parameter Space Services

Chunk Parsing

Statistical Modeling and Learning in Vision --- cortex-like generative models Ying Nian Wu

Statistical NLP Spring 2011

人工知能特論 II

Parsing

Seven Lectures on Statistical Parsing

Parameter tuning based on response surface models An update on work in progress

Generative Models

Building of statistical models

数理言語情報論　第 7 回

RE3/1 & RE4/1 Parameter Space Services