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Now showing items 41-46 of 46
Contech: a shared memory parallel program analysis framework
(Georgia Institute of Technology, 2013-11-19)
We are in the era of multicore machines, where we must exploit thread level parallelism for programs to run better, smarter, faster, and more efficiently. In order to increase instruction level parallelism, processors and ...
Constructing mobile manipulation behaviors using expert interfaces and autonomous robot learning
(Georgia Institute of Technology, 2013-11-19)
With current state-of-the-art approaches, development of a single mobile manipulation capability can be a labor-intensive process that presents an impediment to the creation of general purpose household robots. At the ...
Shared resource management for efficient heterogeneous computing
(Georgia Institute of Technology, 2013-08-01)
The demand for heterogeneous computing, because of its performance and energy efficiency, has made on-chip heterogeneous chip multi-processors (HCMP) become the mainstream computing platform, as the recent trend shows in ...
Analysis of macromolecular structure through experiment and computation
(Georgia Institute of Technology, 2013-04-08)
This thesis covers a wide variety of projects within the domain of computational structural biology. Structural biology is concerned with the molecular structure of proteins and nucleic acids, and the relationship between ...
Cost-effective and privacy-conscious cloud service provisioning: architectures and algorithms
(Georgia Institute of Technology, 2013-05-15)
Cloud Computing represents a recent paradigm shift that enables users to share and remotely access high-powered computing resources (both infrastructure and software/services) contained in off-site data centers thereby ...
On sparse representations and new meta-learning paradigms for representation learning
(Georgia Institute of Technology, 2013-05-15)
Given the "right" representation, learning is easy. This thesis studies representation learning and meta-learning, with a special focus on sparse representations. Meta-learning is fundamental to machine learning, and it ...