PPT-Advantages of gradient-based MCMC algorithms for difficult-

Cole Monnahan 1242015 SAFS Quant Seminar Introduction Bayesian inference is increasingly common in fisheries in ecology There is a need for efficient algorithms for complex models and cross validation of simple models . S . Amari. 11.03.18.(Fri). Computational Modeling of Intelligence. Summarized by . Joon. . Shik. Kim. Abstract. The ordinary gradient of a function does not represent its steepest direction, but the natural gradient does.. Cutting the Computational Budget. Max Welling . (U. Amsterdam / UC Irvine). Collaborators:. Yee . Whye. The . (. University of Oxford). S. . Ahn. ,. A. . Korattikara. , Y. Chen . (PhD students UCI). James S. Strand and David B. Goldstein. The University of Texas at Austin. Sponsored by the Department of Energy through the PSAAP Program. Predictive Engineering and Computational Sciences. Introduction – DSMC Parameters. Clint Jeffery. University of Idaho. Outline. Preliminary thoughts. AIGPW Chapters. EvoGames. Papers. Conclusions. Preliminary Thoughts. ANN and related technologies are rare in commercial games. Behavior of ANN-based agents often perceived as bizarre or unrealistic. Russell Almond . Florida State University. College of Education. Educational Psychology and Learning Systems. ralmond@fsu.edu. BMAW 2014. 1. Cognitive Basis. Multiple cognitive processes involved in writing. Grigory. . Yaroslavtsev. http://grigory.us. Lecture 8: . Gradient Descent. Slides at . http://grigory.us/big-data-class.html. Smooth Convex Optimization. Minimize . over . admits a minimizer . (. Applications. Lecture . 6: . Optimize Finite Sum. Zhu Han. University of Houston. Thanks Dr. . Mingyi. Hong slides. 1. Outline (Chapter 10). Problem Formulation. Algorithms. The SAG and SAGA algorithm [Le Roux 12][. :. Application to Compressed Sensing and . Other Inverse . Problems. M´ario. A. T. . Figueiredo. Robert . D. . Nowak. Stephen . J. Wright. Background. Previous Algorithms. Interior-point method. . Gradient descent. Key Concepts. Gradient descent. Line search. Convergence rates depend on scaling. Variants: discrete analogues, coordinate descent. Random restarts. Gradient direction . is orthogonal to the level sets (contours) of f,. Unconstrained minimization. Steepest descent vs. conjugate gradients. Newton and quasi-Newton methods. Matlab. . fminunc. Unconstrained local minimization. The necessity for one dimensional searches. 1. Neural. . Function. Brain function (thought) occurs . as the result . of . the. . firing . of. . neurons. Neurons . connect . to each . other through . synapses. , . which . propagate . action potential . Unconstrained minimization. Steepest descent vs. conjugate gradients. Newton and quasi-Newton methods. Matlab. . fminunc. Unconstrained local minimization. The necessity for one dimensional searches. Nima Aghaee, Zebo Peng, and Petru Eles. Embedded Systems Laboratory (ESLAB). Linkoping University. 12th Swedish System-on-Chip Conference – May 2013. Outline. Introduction. Early life failures. Temperature gradient effects. Contd. ):. MCMC with Gradients, Recent Advances. CS772A: Probabilistic Machine Learning. Piyush Rai. Plan for today. Some other aspects of MCMC. MCMC with gradient. Some other recent advances. 2. Sampling Methods: Label Switching Issue.