PPT-Feature-Enhanced Probabilistic Models for Diffusion Network Inference

Author : pasty-toler | Published Date : 2018-09-21

Stefano Ermon ECMLPKDD September 26 2012 Joint work with Liaoruo Wang and John E Hopcroft Background Diffusion processes common in many types of networks Cascading

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Feature-Enhanced Probabilistic Models for Diffusion Network Inference: Transcript

Stefano Ermon ECMLPKDD September 26 2012 Joint work with Liaoruo Wang and John E Hopcroft Background Diffusion processes common in many types of networks Cascading examples contact networks ltgt infections. (goal-oriented). Action. Probabilistic. Outcome. Time 1. Time 2. Goal State. 1. Action. State. Maximize Goal Achievement. Dead End. A1. A2. I. A1. A2. A1. A2. A1. A2. A1. A2. Left Outcomes are more likely. in human semantic memory. Mark . Steyvers. , Tomas L. Griffiths, and Simon Dennis. 소프트컴퓨팅연구실. 오근현. TRENDS in Cognitive Sciences vol. . 10, . no. . 7, 2006. Overview . Relational models of memory. for Improved Pipeline Models. Razvan . C. Bunescu. Electrical Engineering and Computer Science. Ohio University. Athens, OH. bunescu@ohio.edu. EMNLP, October 2008. Introduction. 1. Syntactic Parsing. Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Michael Hicks. Piotr (Peter) Mardziel. University of Maryland, College Park. Stephen Magill. Galois. Michael Hicks. UMD. Mudhakar. . Srivatsa. IBM TJ Watson. Jonathan Katz. UMD. Mário. . Alvim. UFMG. Shou-pon. Lin. Advisor: Nicholas F. . Maxemchuk. Department. . of. . Electrical. . Engineering,. . Columbia. . University,. . New. . York,. . NY. . 10027. . Problem: . Markov decision process or Markov chain with exceedingly large state space. a Probabilistic . Lexical . Inference System. . Eyal Shnarch. ,. . Ido . Dagan, Jacob . Goldberger. PLIS - Probabilistic Lexical Inference System. 1. /34. The . entire talk in a single sentence. (M. 3. D). Dr. Brian H. Spitzberg. Principle Investigator: Dr. Ming-Hsiang . Tsou . mtsou@mail.sdsu.edu. ,. . (Geography), . Co-. Pis. : . Dr. . Dipak. K Gupta (Political Science), Dr. Jean Marc Gawron (Linguistic), Dr. Brian . Taisuke. Sato. Tokyo Institute of Technology. Problem. model-specific learning algorithms. Model 1. EM. VB. MCMC. Model 2. Model n. .... .... EM. 1. EM. 2. EM. n. Statistical machine learning is a . Ashish Srivastava. Harshil Pathak. Introduction to Probabilistic Automaton. Deterministic Probabilistic Finite Automata. Probabilistic Finite Automaton. Probably Approximately Correct (PAC) learnability. Machine Learning @ CU. Intro courses. CSCI 5622: Machine Learning. CSCI 5352: Network Analysis and Modeling. CSCI 7222: Probabilistic Models. Other courses. cs.colorado.edu/~mozer/Teaching/Machine_Learning_Courses. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Thesis defense . 4/5/2012. Jaesik Choi. Thesis Committee: . Assoc. Prof. Eyal Amir (Chair, Director of research). Prof. Dan Roth. . Prof. Steven M. Lavalle. Prof. David Poole (University of British Columbia). Learning. Feature Models with. (a.k.a implementing the introductory example). . (. FeAture. Model . scrIpt. . Language. for . manIpulation. and . Automatic. . Reasoning. ) . φ. TVL. DIMACS. http://.

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