PPT-Dataflow Models CPEG421-2001-F-Topic-3-II

2 A BaseLanguage To serve as an intermediatelevel language for highlevel languages To serve as a machine language for parallel machines JB Dennis Data Flow Graphs CPEG4212001FTopic3II. (and the Naiad system). Frank McSherry. , Derek G. Murray,. Rebecca Isaacs, . Michael Isard. Microsoft Research, Silicon Valley. Data-parallel dataflow. 1. 2. 3. 4. 5. 1. 4. 2. 3. 6. 6. 5. A. B. C. D. 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). 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. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Progress Report. New deadline. In class, Thursday February 18. th. (not Tuesday). Outline. 3 to 5 pages maximum. from Text. Padhraic Smyth. Department of Computer Science. University of California, Irvine . . Outline. General aspects of text mining. Named-entity extraction, question-answering systems, etc. Unsupervised learning from text documents. Yuan Lin. 1. , Yoonseo Choi. 1. , Scott Mahlke. 1. , Trevor Mudge. 1. , . Chaitali. Chakrabarti. 2. 1. Advanced Computer Architecture Lab, University of Michigan at Ann Arbor. 2. Department of Electrical Engineering, Arizona State University. James . Foulds. Padhraic. Smyth. Department of Computer Science. University of California, Irvine*. *James . Foulds. has recently moved to the University of California, Santa Cruz. Motivation. Topic model extensions. Derek G. Murray. Michael Isard. Frank McSherry. Paul Barham. Rebecca Isaacs. Martín Abadi. Microsoft Research. 1. Batch processing. Stream processing. Graph processing. Timely dataflow. In. ⋈. #x. Frank McSherry. , Derek G. Murray,. Rebecca Isaacs, Michael Isard. Microsoft Research, Silicon Valley. Data-parallel dataflow. 1. 2. 3. 4. 5. 1. 4. 2. 3. 6. 6. 5. A. B. C. D. E. k1:. k2:. k3:. Data-parallel dataflow. OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). OO. L 2. 0. 12 KY. O. T. O. Briefing & Report. By: Masayuki . Kouno. . (D1) & . Kourosh. . Meshgi. . (D1). Kyoto University, Graduate School of Informatics, Department of Systems Science. Ishii Lab (Integrated System Biology). To perform optimizations like constant propagation or dead code elimination, we must. Analyze program to find opportunities for performing optimizations safely. Transform program. Analysis is called . Shail Dave. 1. , . Youngbin. Kim. 2. , . Sasikanth. Avancha. 3,. Kyoungwoo. Lee. 2. , Aviral Shrivastava. 1. [1] Compiler Microarchitecture Lab, Arizona State University. [2] Department of Computer Science, Yonsei University. Hidden Markov Models. Hidden Markov Models for Time Series. Walter Zucchini. An Introduction to Statistical Modeling. o. f Extreme Values. Stuart Coles. Coles (2001), Zucchini (2016). Nonstationary GEV models.