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» Parameter space exploration with Gaussian process trees
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JMLR
2012
13 years 8 months ago
Random Search for Hyper-Parameter Optimization
Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are ...
James Bergstra, Yoshua Bengio
TKDE
1998
122views more  TKDE 1998»
15 years 5 months ago
The Design and Implementation of Seeded Trees: An Efficient Method for Spatial Joins
—Existing methods for spatial joins require pre-existing spatial indices or other precomputation, but such approaches are inefficient and limited in generality. Operand data sets...
Ming-Ling Lo, Chinya V. Ravishankar
ICASSP
2010
IEEE
15 years 5 months ago
A novel estimation of feature-space MLLR for full-covariance models
In this paper we present a novel approach for estimating featurespace maximum likelihood linear regression (fMLLR) transforms for full-covariance Gaussian models by directly maxim...
Arnab Ghoshal, Daniel Povey, Mohit Agarwal, Pinar ...
SBACPAD
2008
IEEE
170views Hardware» more  SBACPAD 2008»
15 years 12 months ago
Using Analytical Models to Efficiently Explore Hardware Transactional Memory and Multi-Core Co-Design
Transactional memory is emerging as a parallel programming paradigm for multi-core processors. Despite the recent interest in transactional memory, there has been no study to char...
James Poe, Chang-Burm Cho, Tao Li
ICASSP
2008
IEEE
15 years 12 months ago
Adaptation of compressed HMM parameters for resource-constrained speech recognition
Recently, we successfully developed and reported a new unsupervised online adaptation technique, which jointly compensates for additive and convolutive distortions with vector Tay...
Jinyu Li, Li Deng, Dong Yu, Jian Wu, Yifan Gong, A...