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Learning-Based Approach to Estimation of Morphable Model Parameters

dc.date.accessioned2004-10-20T21:04:37Z
dc.date.accessioned2018-11-24T10:23:36Z
dc.date.available2004-10-20T21:04:37Z
dc.date.available2018-11-24T10:23:36Z
dc.date.issued2000-09-01en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/7264
dc.identifier.urihttp://repository.aust.edu.ng/xmlui/handle/1721.1/7264
dc.description.abstractWe describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism exposed by partial evaluation. The Supercomputing Toolkit parallel processor and its associated partial evaluation-based compiler have been used extensively by scientists at MIT, and have made possible recent results in astrophysics showing that the motion of the planets in our solar system is chaotically unstable.en_US
dc.format.extent1037544 bytes
dc.format.extent218112 bytes
dc.language.isoen_US
dc.titleLearning-Based Approach to Estimation of Morphable Model Parametersen_US


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