PPT-Implementing Hierarchical Bayesian Networks on the GPU

Author : maisie | Published Date : 2024-02-16

László Dobos Tamás Budavári Carrie Filion Rosie Wyse Alex Szalay Dept of Physics amp Astronomy The Johns Hopkins University Subaru Prime Focus Spectrograph

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Implementing Hierarchical Bayesian Networks on the GPU: Transcript

László Dobos Tamás Budavári Carrie Filion Rosie Wyse Alex Szalay Dept of Physics amp Astronomy The Johns Hopkins University Subaru Prime Focus Spectrograph Manua. De64257nition A Bayesian nonparametric model is a Bayesian model on an in64257nitedimensional parameter space The parameter space is typically chosen as the set of all possi ble solutions for a given learning problem For example in a regression prob Jun Zhang. , Graham . Cormode. , Cecilia M. . Procopiuc. , . Divesh. . Srivastava. , Xiaokui Xiao. The Problem: Private Data Release. Differential Privacy. Challenges. The Algorithm: PrivBayes. Bayesian Network. Tugba . Koc Emrah Cem Oznur Ozkasap. Department of . Computer . Engineering, . Koç . University. , Rumeli . Feneri Yolu, Sariyer, Istanbul . 34450 Turkey. Introduction. Epidemic (gossip-based) principles: highly popular in large scale distributed systems. Week 9 and Week 10. 1. Announcement. Midterm II. 4/15. Scope. Data . warehousing and data cube. Neural . network. Open book. Project progress report. 4/22. 2. Team Homework Assignment #11. Read pp. 311 – 314.. and Games in Simulation . Metamodeling. Jirka. . Poropudas. (M.Sc.). Aalto University. School of Science and Technology. Systems Analysis Laboratory. http://www.sal.tkk.fi/en/. jirka.poropudas@tkk.fi . on. Bayesian. . Techniques. for. . Inference. Asensio Ramos. Instituto de Astrofísica de Canarias. Outline. General . introduction. . . The. . Bayesian. . approach. . to. . inference. . Examples. Large Scale Visual Recognition Challenge (ILSVRC) 2013:. Detection spotlights. Toronto A team. Latent Hierarchical Model with GPU Inference for Object Detection. Yukun Zhu, Jun Zhu, Alan Yuille . UCLA Computer Vision Lab. Author: David Heckerman. . Presented By:. Yan Zhang - 2006. Jeremy Gould – 2013. Chip Galusha -2014. 1. Outline. Bayesian Approach. Bayesian vs. classical probability methods. Bayes. . Theorm. Oliver van . Kaick. 1,4 . . Kai . Xu. 2. . Hao. Zhang. 1. . Yanzhen. Wang. 2. . Shuyang. Sun. 1. Ariel Shamir. 3. Daniel Cohen-Or. 4. 4. Tel Aviv University. 1. Simon . Fraser University. or. How to combine data, evidence, opinion and guesstimates to make decisions. Information Technology. Professor Ann Nicholson. Faculty of Information Technology. Monash University . (Melbourne, Australia). Avdesh. Mishra, . Manisha. . Panta. , . Md. . Tamjidul. . Hoque. , Joel . Atallah. Computer Science and Biological Sciences Department, University of New Orleans. Presentation Overview. 4/10/2018. CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) IEOR 8100.003 Final Project. 9. th. May 2012. Daniel Guetta. Joint work with Carri Chan. This talk. Hospitals. Bayesian Networks. Data!. Modified EM Algorithm. First results. Instrumental variables. Neil Bramley. Intro. 1. Limitations of Causal . Bayes. Nets as psychological models.. 2. Extension of the approach using the hierarchical Bayesian framework.. 3. Philosophical implications of this framework.

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