PPT-Lifted Inference for Relational Hybrid Models

Author : karlyn-bohler | Published Date : 2017-12-20

Thesis defense 452012 Jaesik Choi Thesis Committee Assoc Prof Eyal Amir Chair Director of research Prof Dan Roth Prof Steven M Lavalle Prof David Poole University

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Lifted Inference for Relational Hybrid Models: Transcript

Thesis defense 452012 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. Xiao Cheng . and. Dan Roth. University of Illinois at Urbana-Champaign. 1. Wikification. 2. Blumenthal (D) is a candidate for the U.S. Senate seat now held by Christopher Dodd (D), and he has held a commanding lead in the race since he entered it. But the Times report has the potential to fundamentally reshape the contest in the Nutmeg State.. Graphical Model Inference. View observed data and unobserved properties as . random variables. Graphical Models: compact graph-based encoding of probability distributions (high dimensional, with complex dependencies). 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. Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Daniel Lowd. University of Oregon. April 20, 2015. Caveats. The purpose of this talk is to inspire meaningful discussion.. I may be completely wrong.. My background:. Markov logic networks, probabilistic graphical models. Source: “Topic models”, David . Blei. , MLSS ‘09. Topic modeling - Motivation. Discover topics from a corpus . Model connections between topics . Model the evolution of topics over time . Image annotation. Chapter 5 . The Normal Distribution. Univariate. Normal Distribution. For short we write:. Univariate. normal distribution describes single continuous variable.. Takes 2 parameters . m. and . s. 2. With thanks to: . Parisa . Kordjamshidi, Avi Pfeffer, Guy Van den . Broeck. , Sameer Singh, . Vivek Srikumar, Rodrigo de Salvo Braz,. . Nick Rizzolo . . Declarative . Learning Based Programming. Rodrigo de Salvo Braz. Ciaran O’Reilly. Artificial Intelligence Center - SRI International. Vibhav Gogate. University of Texas at Dallas. Rina Dechter. University of California, Irvine. IJCAI-16. , . Xiao Cheng . and. Dan Roth. University of Illinois at Urbana-Champaign. 1. Wikification. 2. Blumenthal (D) is a candidate for the U.S. Senate seat now held by Christopher Dodd (D), and he has held a commanding lead in the race since he entered it. But the Times report has the potential to fundamentally reshape the contest in the Nutmeg State.. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Chapter 19 . Temporal models. 2. Goal. To track object state from frame to frame in a video. Difficulties:. Clutter (data association). One image may not be enough to fully define state. Relationship between frames may be complicated. Sriraam Natarajan. Dept of . Computer Science, . University . of . Wisconsin-Madison. Take-Away Message . Inference. in SRL Models is . very hard. !!!!. This talk – Presents . 3 different yet related. Patterson, P. L., Healey, S. P., St

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