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ICML
2009
IEEE
16 years 7 months ago
Large-scale deep unsupervised learning using graphics processors
The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free paramete...
Rajat Raina, Anand Madhavan, Andrew Y. Ng
IPPS
2000
IEEE
15 years 11 months ago
Reduction Optimization in Heterogeneous Cluster Environments
Network of workstation (NOW) is a cost-effective alternative to massively parallel supercomputers. As commercially available off-the-shelf processors become cheaper and faster, ...
Pangfeng Liu, Da-Wei Wang
IPPS
2010
IEEE
15 years 4 months ago
On the parallelisation of MCMC-based image processing
Abstract--The increasing availability of multi-core and multiprocessor architectures provides new opportunities for improving the performance of many computer simulations. Markov C...
Jonathan M. R. Byrd, Stephen A. Jarvis, Abhir H. B...
CVPR
2005
IEEE
16 years 8 months ago
Spectral Segmentation with Multiscale Graph Decomposition
We present a multiscale spectral image segmentation algorithm. In contrast to most multiscale image processing, this algorithm works on multiple scales of the image in parallel, w...
Florence Bénézit, Jianbo Shi, Timoth...
ICCV
2007
IEEE
16 years 8 months ago
Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction
Large-scale 3D reconstruction has recently received much attention from the computer vision community. Bundle adjustment is a key component of 3D reconstruction problems. However,...
Kai Ni, Drew Steedly, Frank Dellaert