PPT-Models of networks (synthetic networks or generative models

Prof Ralucca Gera Applied Mathematics Dept Naval Postgraduate School Monterey California rgeranpsedu Excellence Through Knowledge Learning Outcomes I dentify network models and explain their structures. Alan Ritter. Markov Networks. Undirected. graphical models. Cancer. Cough. Asthma. Smoking. Potential functions defined over cliques. Smoking. Cancer. . Ф. (S,C). False. False. 4.5. False. True. 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. Haenggi. et al. EE 360 : 19. th. February 2014. . Contents. SNR, SINR and geometry. Poisson Point Processes. Analysing interference and outage. Random Graph models. Continuum percolation and network models. vs. Discriminative models. Roughly:. Discriminative. Feedforw. ard. Bottom-up. Generative. Feedforward recurrent feedback. Bottom-up horizontal top-down. Compositional . generative models require a flexible, “universal,” representation format for relationships.. Carolyn J. Anderson, Stanley Wasserman & Bradley Crouch (1999). 1. Predictive Models: Problems. Relationship specific social relation – explanatory variables. Response variable dichotomous/discrete (actor . Erdős-Rényi. Random model, . Watts-. Strogatz. Small-world, . Barabási. -Albert Preferential attachment, . Molloy-Reed . Configuration model . and . Gilbert . Random . G. eometric model. Excellence Through Knowledge. Kenneth Frank, College of Education and Fisheries and Wildlife. Help from: Ann Krause, Ben Michael Pogodzinski, Bo Yan, Min Sun, I-Chen, Chong Min Kim. Cep 991B Fall 2018. To participate on zoom you will click on . Akrit Mohapatra. ECE Department, Virginia Tech. What are GANs?. System of . two neural networks competing against each other in a zero-sum game framework. . They were first introduced by . Ian Goodfellow. B. . Aditya. . Prakash. http://www.cs.cmu.edu/~badityap. Carnegie Mellon University. MMS, SIAM AN, Minneapolis, July 10, 2012. Thanks !. Jeremy . Kepner. David . Bader. John . Gilbert. 2. Networks are everywhere!. An Overview. Yidong. Chai. 1,2. , . Weifeng Li. 1,3. , Hsinchun Chen. 1. 1 . Artificial Intelligence Laboratory, The University of Arizona. 2 . Tsinghua University. 3 . University of Georgia. 1. Acknowledgements. Ari Zitin (University of North Carolina), Alex . Gorowara. (Worcester Polytechnic Institute). S. Squires, M. Herrera, T. . Antonsen. , M. Girvan, E. . Ott. (University of Maryland). Image Credit to transductions.net. The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand Nisheeth. Coin toss example. Say you toss a coin N times. You want to figure out its bias. Bayesian approach. Find the generative model. Each toss ~ Bern(. θ. ). θ. ~ Beta(. α. ,. β. ). Draw the generative model in plate notation. Machine Learning Department. Carnegie Mellon University. Cyberspace. The emergence of ‘cyberspace’ and the World Wide Web is like the discovery of a new continent.. Jim Gray, 1998 Turing Award address.