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» Parameter space exploration with Gaussian process trees
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JMLR
2012
11 years 10 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»
13 years 7 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
13 years 7 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»
14 years 1 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
14 years 2 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...