PPT-Markov Nets Dhruv Batra

10708 Recitation 10 30 2008 Contents MRFs Semantics Comparisons with BNs Applications to vision HW4 implementation Semantics Bayes Nets Semantics Markov Nets Semantics Decomposition. T state 8712X action or input 8712U uncertainty or disturbance 8712W dynamics functions XUW8594X w w are independent RVs variation state dependent input space 8712U 8838U is set of allowed actions in state at time brPage 5br Policy action is function (Markov Nets). (Slides from Sam . Roweis. ). Connection to MCMC:. . . MCMC requires sampling a node given its . markov. blanket. . Need to use P(. x|MB. (x)). . . For . Bayes. nets MB(x) contains more. Nick Russell. Arthur H. M. . ter. . Hofstede. Acknowledgement. These slides . summarize the content of Chapter 2 of the book:. A.H.M. ter Hofstede, W. van der Aalst, M. Adams, N. Russell.. Modern . Business Process . Model Definition. Comparison to Bayes Nets. Inference techniques. Learning Techniques. A. B. C. D. Qn. : What is the. . most likely. . configuration of A&B?. Factor says a=b=0. But, marginal says. A two- year comprehensive project of Virginia and Ghana Baptists. to. . Reduce Malaria. and. S. upport Ministry and Church Planting . in . Yendi, Ghana. Where is GHANA?. Where will the . nets go? . Sibel Adali, . Sujoy Sikdar. , Lirong Xia. Multi-Issue Voting. { , } . X. . { , }. Wine (. ).  . Main dishes (. ).  . Goal: Cater to people’s preferences. issues. Local Primal-Dual Gaps. Dhruv Batra (TTIC). Joint work with: . Daniel . Tarlow. (U Toronto), Sebastian . Nowozin. (MSRC), . Pushmeet. . Kohli. (MSRC), Vladimir . Kolmogorov. (UCL). Overview. Discrete . Larry . Zitnick. (Facebook AI Research). Devi Parikh. (Virginia Tech). Stanislaw . Antol. (Virginia Tech). Aishwarya Agrawal. (Virginia Tech). Overview of Challenge . Outline. Overview of Task and Dataset . Georgia Tech. Topics. : . Toeplitz matrices and convolutions = matrix-. mult. Dilated/a-. trous. . convolutions. Backprop. in conv layers. Transposed convolutions. Administrativia. HW1 extension. . ECE6504 – Deep Learning for Perception Ashwin Kalyan V Introduction to CAFFE (C) Dhruv Batra 2 Logistic Regression as a Cascade (C) Dhruv Batra 3 Slide Credit: Marc'Aurelio Ranzato , Yann LeCun 1980, he earned his Juris Doctor degree from the Fordham University School of Law. 2007, he chairs the National Advisory on South Asian Affairs, a foreign policy think tank, and interacts with the ASEEPO R T part of ASEEPO future science group ASEEPOSaha, Dubey, Kapoor & Batra www.futuremedicine.com future science group Widely used throughout today’s construction industry, safety nets are an integral aspect of health and safety on busy, often dangerous, construction sites. Though inexpensive as they are, safety nets remain an incredibly effective means of providing anyone on site with a safe way to work without the fear of falling or being struck by debris. Deep Learning Introduction Thanks to Dhruv Batra Georgia Tech http://clgiles.ist.psu.edu/IST597 Class Course outline and materials at https://clgiles.ist.psu.edu/IST597 5 exercises, all in TensorFlow/Python - 40% One large