PPT-Type Inference with Run-time Logs

Author : marina-yarberry | Published Date : 2018-03-17

Ravi Chugh Motivation Dynamic Languages Dynamicallytyped languages Enable rapid prototyping Facilitate interlanguage development Staticallytyped languages Prevent

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Type Inference with Run-time Logs: Transcript

Ravi Chugh Motivation Dynamic Languages Dynamicallytyped languages Enable rapid prototyping Facilitate interlanguage development Staticallytyped languages Prevent certain runtime errors Enable optimized execution. in type-based verification. Dimitrios Vytiniotis, Microsoft Research. dimitris@microsoft.com. . May 2010. Software is hard to get right*. Which . tools. can help programmers write reliable code?. How to make these tools . Kathleen Fisher. cs242. Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course (M/EEG). London, May 14, 2013. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Sai . Vallurupalli. What are query logs useful for?. In Social Sciences, Medical & Health, Advertising & Marketing, Law Enforcement etc. . Understanding Search Behavior – Trends and Hot Trends. Chapter 14 . The pinhole camera. Structure. Pinhole camera model. Three geometric problems. Homogeneous coordinates. Solving the problems. Exterior orientation problem. Camera calibration. 3D reconstruction. Daniel R. Schlegel and Stuart C. Shapiro. <. drschleg,shapiro. >@buffalo.edu. Department of Computer Science and Engineering. L. A. – Logic of Arbitrary and Indefinite Objects. 2. Logic in Cognitive Systems. Chris . Mathys. Wellcome Trust Centre for Neuroimaging. UCL. SPM Course. London, May 11, 2015. Thanks to Jean . Daunizeau. and . Jérémie. . Mattout. for previous versions of this talk. A spectacular piece of information. Sergio Pissanetzky. Sergio@SciControls.com. Emergent Inference. Any system. VISION. ROBOT. SOFTWARE. your mom. grab. an. object. computer. program. eyes. cameras,. sensors. translation. 100,000,000. Daniel R. Schlegel and Stuart C. Shapiro. Department of Computer Science and Engineering. University at Buffalo, The State University of New York. Buffalo, New York, USA. <. drschleg,shapiro. >@buffalo.edu. Warm up. Share your picture with the people at your table group.. Make sure you have your Science notebook, agenda and a sharpened pencil. use tape to put it in front of your table of contents. Describe the difference between observations and inferences. Parallel Programs. Harish Patil, . Cristiano Pereira. , Mack Stallcup, Gregory Lueck, James Cownie. Intel Corporation. CGO 2010, Toronto, Canada. 1. Non-Determinism. Program execution is not repeatable across runs. Ravi Chugh. Motivation: Dynamic Languages. Dynamically-typed languages. Enable rapid prototyping. Facilitate inter-language development. Statically-typed languages. Prevent certain run-time errors. Enable optimized execution. Chapter . 2 . Introduction to probability. Please send errata to s.prince@cs.ucl.ac.uk. Random variables. A random variable . x. denotes a quantity that is uncertain. May be result of experiment (flipping a coin) or a real world measurements (measuring temperature). Reading: “Concepts in Programming Languages”, Chapter 6. . . Outline. General discussion of types. What is a type?. Compile-time . vs. run-time checking. Conservative program analysis.

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