'Probabilistic inference' diaporamas de présentation

Tutorial on Bayesian Techniques for Inference

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Querying a Million Genomes in less than a millisecond?

Interpreting the Genome Technology Review 2009 New technologies will soon make it possible to sequence thousands of human genomes. Now comes the hard part: understanding all the data. Querying a Million Genomes in less than a millisecond?. George Varghese (MSR, UCSD)

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On Distributing Probabilistic Inference

On Distributing Probabilistic Inference. Metron, Inc. Thor Whalen. Outline. Inference and distributed inference Conditional independence (CI) Graphical models, CI, and separability Using CI: Sufficient information. Outline. Inference and distributed inference Probabilistic inference

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Advanced Artificial Intelligence

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Montague meets Markov: Combining Logical and Distributional Semantics

Montague meets Markov: Combining Logical and Distributional Semantics. Raymond J. Mooney Katrin Erk Islam Beltagy University of Texas at Austin. 1. 1. Logical AI Paradigm. Represents knowledge and data in a binary symbolic logic such as FOPC.

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Inferring ancestral states of the bZIP transcription factor interaction network

Inferring ancestral states of the bZIP transcription factor interaction network. John Pinney. Faculty of Life Sciences University of Manchester, UK. Networks in computational biology. The genotype ? phenotype relationship is mediated by many inter-related biochemical networks.

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CS 570 Artificial Intelligence Chapter 20. Bayesian Learning

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QuASI: Question Answering using Statistics, Semantics, and Inference

QuASI: Question Answering using Statistics, Semantics, and Inference. Marti Hearst, Jerry Feldman, Chris Manning, Srini Narayanan Univ. of California-Berkeley / ICSI / Stanford University. Dynamic Probabilistic Inference for event structure. Srini Narayanan Jerry Feldman

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??????????? ??????????????????. ???? ?????????????? E-mail: kazu@smapip.is.tohoku.ac.jp. Information Processing and Many Body Problem in Quantum Statistical Mechanics. Quantum Hopfield Model (H. Nishimori) Quantum Annealing (H. Nishimori)

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Probabilistic inference in human semantic memory Mark Steyvers , Tomas L. Griffiths, and Simon Dennis

TRENDS in Cognitive Sciences vol. 10, no. 7, 2006. Probabilistic inference in human semantic memory Mark Steyvers , Tomas L. Griffiths, and Simon Dennis. ????????? ???. Overview . Relational models of memory The Retrieving Effectively from Memory(REM) Model

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Robotics

Robotics. R&N: ch 25. based on material from Jean-Claude Latombe, Daphne Koller, Stuart Russell. sensors. environment. ?. agent. effectors. Agent. Robots ? Physical sensors and effectors. Sensors. Sensors that tell the robot position/change of joints: odometers, speedometers, etc.

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Kazuyuki Tanaka Graduate School of Information Sciences Tohoku University, Japan

Statistical-Mechanical Approach to Probabilistic Inference --- Cluster Variation Method and Generalized Loopy Belief Propagation ---. Kazuyuki Tanaka Graduate School of Information Sciences Tohoku University, Japan kazu@statp.is.tohoku.ac.jp

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Bayesian Networks (cont.)

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Presented by: Mingyuan Zhou Duke University, ECE September 18, 2009

Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng ICML 2009. Presented by: Mingyuan Zhou Duke University, ECE September 18, 2009. Outline. Motivations Contributions

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Latent Tree Models & Statistical Foundation for TCM

Latent Tree Models & Statistical Foundation for TCM. Nevin L. Zhang Joint Work with: Chen Tao, Wang Yi, Yuan Shihong Department of Computer Science & Engineering The Hong Kong University of Science & Technology http://www.cse.ust.hk/~lzhang/. Publications.

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Probabilistic Inference in PRISM

Probabilistic Inference in PRISM. Taisuke Sato Tokyo Institute of Technology. Problem. Statistical machine learning is a labor-intensive process: { modeling ? learning ? evaluation}* of trial-and-error

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HUME ON THE ARGUMENT FROM DESIGN ( Part 2 of 2 )

HUME ON THE ARGUMENT FROM DESIGN ( Part 2 of 2 ). Text source: Dialogues Concerning Natural Religion , parts 2-8. THE ARGUMENT FROM DESIGN ( advocated by Cleanthes ). 1. The Universe and many of the things in it resemble artifacts.

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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

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Neural Bayesian Inference

Neural Bayesian Inference. Pecevski , Buesing , and Maass , 2011: Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons.

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