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pro vyhledávání: '"Garakani, Alireza Bagheri"'
In this paper, we introduce an Augmented Lagrangian based method to incorporate the multiple objectives (MO) in a search ranking algorithm. Optimizing MOs is an essential and realistic requirement for building ranking models in production. The propos
Externí odkaz:
http://arxiv.org/abs/2002.05753
Autor:
May, Avner, Garakani, Alireza Bagheri, Lu, Zhiyun, Guo, Dong, Liu, Kuan, Bellet, Aurélien, Fan, Linxi, Collins, Michael, Hsu, Daniel, Kingsbury, Brian, Picheny, Michael, Sha, Fei
We study large-scale kernel methods for acoustic modeling in speech recognition and compare their performance to deep neural networks (DNNs). We perform experiments on four speech recognition datasets, including the TIMIT and Broadcast News benchmark
Externí odkaz:
http://arxiv.org/abs/1701.03577
Autor:
Lu, Zhiyun, Guo, Dong, Garakani, Alireza Bagheri, Liu, Kuan, May, Avner, Bellet, Aurelien, Fan, Linxi, Collins, Michael, Kingsbury, Brian, Picheny, Michael, Sha, Fei
We study large-scale kernel methods for acoustic modeling and compare to DNNs on performance metrics related to both acoustic modeling and recognition. Measuring perplexity and frame-level classification accuracy, kernel-based acoustic models are as
Externí odkaz:
http://arxiv.org/abs/1603.05800
Autor:
Lu, Zhiyun, May, Avner, Liu, Kuan, Garakani, Alireza Bagheri, Guo, Dong, Bellet, Aurélien, Fan, Linxi, Collins, Michael, Kingsbury, Brian, Picheny, Michael, Sha, Fei
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively overcome. In particular, we develop methods to scale up kernel models to s
Externí odkaz:
http://arxiv.org/abs/1411.4000
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to be combined are not centrally located but spread over a network. We add
Externí odkaz:
http://arxiv.org/abs/1404.2644
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