'Generative models' diaporamas de présentation

2007 NIPS Tutorial on: Deep Belief Nets

2007 NIPS Tutorial on: Deep Belief Nets. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Some things you will learn in this tutorial.

1.62k views • 100 slides

2007 NIPS Tutorial on: Deep Belief Nets

2007 NIPS Tutorial on: Deep Belief Nets. Geoffrey Hinton Canadian Institute for Advanced Research & Department of Computer Science University of Toronto. Some things you will learn in this tutorial.

1.15k views • 100 slides

Part II: How to make a Bayesian model

1.12k views • 80 slides

Bayesian models of inductive learning

928 views • 74 slides

733 views • 57 slides

Generative Topic Models for Community Analysis

Generative Topic Models for Community Analysis . Ramesh Nallapati. Objectives. Provide an overview of topic models and their learning techniques Mixture models, PLSA, LDA EM, variational EM, Gibbs sampling Convince you that topic models are an attractive framework for community analysis

754 views • 55 slides

Unsupervised Structure Prediction with Non-Parallel Multilingual Guidance

Unsupervised Structure Prediction with Non-Parallel Multilingual Guidance. Shay B. Cohen Dipanjan Das Noah A. Smith Carnegie Mellon University. July 27 EMNLP 2011. Goal: . Learn linguistic structure for a language without any labeled data in that language.

652 views • 54 slides

Graphical Models in Vision .

606 views • 43 slides

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.

631 views • 40 slides

Generative and Discriminative Models in NLP: A Survey

827 views • 39 slides

1. Local Spectral Methods 2. Community Structure in Large Social and Information Networks

442 views • 38 slides

Advanced Topics in Data Mining Special focus: Social Networks

Advanced Topics in Data Mining Special focus: Social Networks. Reminders. By the end of this week/ beginning of next we need to have a tentative presentation schedule Each one of you should send me an email about a theme by Friday, February 22. What did we learn in the last lecture?.

541 views • 38 slides

Advanced Topics in Data Mining Special focus: Social Networks

Advanced Topics in Data Mining Special focus: Social Networks. Reminders. By the end of this week/ beginning of next we need to have a tentative presentation schedule Each one of you should send me an email about a theme by Friday, February 22. What did we learn in the last lecture?.

505 views • 38 slides

Neuromorphic Square, or, where Predictive Processing and Robotics meet

Neuromorphic Square, or, where Predictive Processing and Robotics meet Johan Kwisthout, Donders Center for Cognition. Robotics Square, or, where Predictive Processing and Neuromorphic meet Johan Kwisthout, Donders Center for Cognition.

557 views • 36 slides

Contagious Diseases in Social Networks

Contagious Diseases in Social Networks. Epidemiology Models. SIS Model. A healthy vertex because infected at a rate proportional to An infected vertex becomes healthy at rate 1 Question: What does this process do on different types of graphs? Does it exist forever? Does it die out?

473 views • 34 slides

Improved Index Compression Techniques for Versioned Document Collections

Improved Index Compression Techniques for Versioned Document Collections. Jinru He, Junyuan Zeng, and Torsten Suel Computer Science & Engineering Polytechnic Institute of NYU. Content of this Talk. Introduction Related work Our improved approaches Conclusion and future work. Introduction.

640 views • 30 slides

Introduction to Text Mining

630 views • 29 slides

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. John Lafferty Andrew McCallum Fernando Pereira. Goal: Sequence segmentation and labeling. Computational biology Computational linguistics Computer science. Overview. Generative models

444 views • 26 slides

313 views • 18 slides

Agenda

Agenda. Introduction Bag-of-words model Visual words with spatial location Part-based models Discriminative methods Segmentation and recognition Recognition-based image retrieval Datasets & Conclusions. Lazebnik, Schmid & Ponce, 2006. Slide credit: S. Lazebnik. Slide credit: S. Lazebnik.

244 views • 6 slides