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CAV
2009
Springer
134views Hardware» more  CAV 2009»
16 years 6 months ago
Predecessor Sets of Dynamic Pushdown Networks with Tree-Regular Constraints
Abstract. Dynamic Pushdown Networks (DPNs) are a model for parallel programs with (recursive) procedures and process creation. The goal of this paper is to develop generic techniqu...
Alexander Wenner, Markus Müller-Olm, Peter La...
RECOMB
2007
Springer
16 years 6 months ago
Learning Gene Regulatory Networks via Globally Regularized Risk Minimization
Learning the structure of a gene regulatory network from time-series gene expression data is a significant challenge. Most approaches proposed in the literature to date attempt to ...
Yuhong Guo, Dale Schuurmans
TSMC
2008
99views more  TSMC 2008»
15 years 6 months ago
Robust Regularized Kernel Regression
Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual fo...
Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu
ACL
2009
15 years 4 months ago
Stochastic Gradient Descent Training for L1-regularized Log-linear Models with Cumulative Penalty
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
Yoshimasa Tsuruoka, Jun-ichi Tsujii, Sophia Anania...
ICASSP
2011
IEEE
14 years 10 months ago
A Lagrangian dual relaxation approach to ML MIMO detection: Reinterpreting regularized lattice decoding
This paper describes a new approximate maximum-likelihood (ML) MIMO detection approach by studying a Lagrangian dual relaxation (LDR) of ML. Unlike many existing relaxed ML method...
Jiaxian Pan, Wing-Kin Ma