Now showing items 551-556 of 556
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 ...
Towards practical fully homomorphic encryption
(Georgia Institute of Technology, 2015-07-24)
Fully homomorphic encryption (FHE) allows for computation of arbitrary func- tions on encrypted data by a third party, while keeping the contents of the encrypted data secure. This area of research has exploded in recent ...
Learning matrix and functional models in high-dimensions
(Georgia Institute of Technology, 2014-06-20)
Statistical machine learning methods provide us with a principled framework for extracting meaningful information from noisy high-dimensional data sets. A significant feature of such procedures is that the inferences made ...
Modern aspects of unsupervised learning
(Georgia Institute of Technology, 2014-06-23)
Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied task arising in many applications ranging from computer ...
A distributed framework for situation awareness on camera networks
(Georgia Institute of Technology, 2014-07-01)
With the proliferation of cameras and advanced video analytics, situation awareness applications that automatically generate actionable knowledge from live camera streams has become an important class of applications in ...
The roles of allocentric representations in autonomous local navigation
(Georgia Institute of Technology, 2015-02-20)
In this thesis, I study the computational advantages of the allocentric represen- tation as compared to the egocentric representation for autonomous local navigation. Whereas in the allocentric framework, all variables of ...