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» Optimizing for parallelism and data locality
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ICIP
2009
IEEE
16 years 7 months ago
Weighted Average Denoising With Sparse Orthonormal Transforms
Sparse Orthonormal Transforms (SOT) has recently been proposed as a data compression method that can achieve sparser representations in transform domain. Given initial conditions,...
ICRA
2005
IEEE
126views Robotics» more  ICRA 2005»
15 years 12 months ago
Global A-Optimal Robot Exploration in SLAM
— It is well-known that the Kalman filter for simultaneous localization and mapping (SLAM) converges to a fully correlated map in the limit of infinite time and data [1]. Howev...
Robert Sim, Nicholas Roy
ARC
2008
Springer
112views Hardware» more  ARC 2008»
15 years 8 months ago
Lossless Compression for Space Imagery in a Dynamically Reconfigurable Architecture
Abstract. This paper presents a novel dynamically reconfigurable hardware architecture for lossless compression and its optimization for space imagery. The proposed system makes us...
Xiaolin Chen, Cedric Nishan Canagarajah, Raffaele ...
JMLR
2012
13 years 8 months ago
Deterministic Annealing for Semi-Supervised Structured Output Learning
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on unlabeled data to deal with the combinatorial nature ...
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Be...
KDD
2005
ACM
149views Data Mining» more  KDD 2005»
15 years 11 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh