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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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Dimitris C. Tsamatsoulis, Christos A. Korologos and Dimitris V. Tsiftsoglou
This study aims to approximate the optimum sulfate content of cement, applying maximization of compressive strength as a criterion for cement produced in industrial mills. The design includes tests on four types of cement containing up to three main comp...
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S. Indrapriyadarsini, Shahrzad Mahboubi, Hiroshi Ninomiya, Takeshi Kamio and Hideki Asai
Gradient-based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving hi...
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Zabidin Salleh, Ghaliah Alhamzi, Ibitsam Masmali and Ahmad Alhawarat
The conjugate gradient method is one of the most popular methods to solve large-scale unconstrained optimization problems since it does not require the second derivative, such as Newton?s method or approximations. Moreover, the conjugate gradient method ...
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Kunal Banerjee,Evangelos Georganas,Dhiraj D. Kalamkar,Barukh Ziv,Eden Segal,Cristina Anderson,Alexander Heinecke
Pág. 64 - 85
Recurrent neural network (RNN) models have been found to be well suited for processing temporal data. In this work, we present an optimized implementation of vanilla RNN cell and its two popular variants: LSTM and GRU for Intel Xeon architecture. Typical...
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