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Tushar Ganguli and Edwin K. P. Chong
We present a novel technique for pruning called activation-based pruning to effectively prune fully connected feedforward neural networks for multi-object classification. Our technique is based on the number of times each neuron is activated during model...
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Ting Guo, Nurmemet Yolwas and Wushour Slamu
Recently, the performance of end-to-end speech recognition has been further improved based on the proposed Conformer framework, which has also been widely used in the field of speech recognition. However, the Conformer model is mostly applied to very wid...
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Ferenc Izsák and Taki Eddine Djebbar
We propose neural-network-based algorithms for the numerical solution of boundary-value problems for the Laplace equation. Such a numerical solution is inherently mesh-free, and in the approximation process, stochastic algorithms are employed. The chief ...
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Kun Wang, Defu Jiang, Lijun Yun and Xiaoyang Liu
Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.
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Gaoyuan Cai, Juhu Li, Xuanxin Liu, Zhibo Chen and Haiyan Zhang
Recently, the deep neural network (DNN) has become one of the most advanced and powerful methods used in classification tasks. However, the cost of DNN models is sometimes considerable due to the huge sets of parameters. Therefore, it is necessary to com...
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Sihmehmet Yildiz, Hayriye Pehlivan Solak and Melike Nikbay
Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase. Single and multi-fidelity surrogate modelling methods have been used to replace the expensive hi...
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Nikita Zvonarev and Nina Golyandina
The method of alternating projections for extracting low-rank signals is considered. The problem of decreasing the computational costs while keeping the estimation accuracy is analyzed. The proposed algorithm consists of alternating projections on the se...
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Siriwan Intawichai and Saifon Chaturantabut
An accelerated least-squares approach is introduced in this work by incorporating a greedy point selection method with randomized singular value decomposition (rSVD) to reduce the computational complexity of missing data reconstruction. The rSVD is used ...
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Filippo Zanetti and Luca Bergamaschi
We review a number of preconditioners for the advection-diffusion operator and for the Schur complement matrix, which, in turn, constitute the building blocks for Constraint and Triangular Preconditioners to accelerate the iterative solution of the discr...
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Shuting Cai, Qilun Luo, Ming Yang, Wen Li and Mingqing Xiao
Tensor Robust Principal Component Analysis (TRPCA) plays a critical role in handling high multi-dimensional data sets, aiming to recover the low-rank and sparse components both accurately and efficiently. In this paper, different from current approach, w...
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