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|a Rinard, Martin C.
|e author
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Rinard, Martin C.
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|a Rinard, Martin C.
|e contributor
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|a Probabilistic Accuracy Bounds for Perforated Programs: A New Foundation for Program Analysis and Transformation
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|b Association for Computing Machinery (ACM),
|c 2012-08-29T20:20:03Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/72443
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|a Traditional program transformations operate under the onerous constraint that they must preserve the exact behavior of the transformed program. But many programs are designed to produce approximate results. Lossy video encoders, for example, are designed to give up perfect fidelity in return for faster encoding and smaller encoded videos [10]. Machine learning algorithms usually work with probabilistic models that capture some, but not all, aspects of phenomena that are difficult (if not impossible) to model with complete accuracy [2]. Monte-Carlo computations use random simulation to deliver inherently approximate solutions to complex systems of equations that are, in many cases, computationally infeasible to solve exactly [5].
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|a en_US
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|a Article
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|t Proceedings of the 20th ACM SIGPLAN Workshop on Partial Evaluation and Program Manipulation (PEPM '11)
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