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» Quantization of Sparse Representations
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NIPS
2008
13 years 9 months ago
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from audio, image, and video data. Recent research has be...
Julien Mairal, Francis Bach, Jean Ponce, Guillermo...
NIPS
2004
13 years 9 months ago
Sparse Coding of Natural Images Using an Overcomplete Set of Limited Capacity Units
It has been suggested that the primary goal of the sensory system is to represent input in such a way as to reduce the high degree of redundancy. Given a noisy neural representati...
Eizaburo Doi, Michael S. Lewicki
EUROPAR
1997
Springer
13 years 12 months ago
A Relational Approach to the Compilation of Sparse Matrix Programs
Abstract. We present a relational algebra based framework for compiling e cient sparse matrix code from dense DO-ANY loops and a speci cation of the representation of the sparse ma...
Vladimir Kotlyar, Keshav Pingali, Paul Stodghill
CVPR
2012
IEEE
11 years 10 months ago
Geometry constrained sparse coding for single image super-resolution
The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse codingbased image super-resolution. Sparse coding is a typical unsupervi...
Xiaoqiang Lu, Haoliang Yuan, Pingkun Yan, Yuan Yua...
CC
2008
Springer
124views System Software» more  CC 2008»
13 years 9 months ago
Coqa: Concurrent Objects with Quantized Atomicity
This paper introduces a new language model, Coqa, for deeply embedding concurrent programming into objects. Every program written in our language has the desirable behaviors of ato...
Yu David Liu, Xiaoqi Lu, Scott F. Smith