Zobrazeno 1 - 10
of 633
pro vyhledávání: '"Kay Chen Tan"'
Publikováno v:
Complex & Intelligent Systems, Vol 9, Iss 2, Pp 1115-1116 (2023)
Externí odkaz:
https://doaj.org/article/2a314fe4f5204731b0ad5f47a0997707
Publikováno v:
IEEE Access, Vol 7, Pp 7133-7146 (2019)
D-optimal designs are frequently used in controlled experiments to obtain the most accurate estimate of model parameters at minimal cost. Finding them can be a challenging task, especially when there are many factors in a nonlinear model. As the numb
Externí odkaz:
https://doaj.org/article/b2e1336c16d04c86acb2105f4b279a31
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The
Externí odkaz:
https://doaj.org/article/c96f6d48c94e43729ea9ac8ad31628a5
Publikováno v:
Frontiers in Neuroscience, Vol 12 (2018)
Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, th
Externí odkaz:
https://doaj.org/article/ca4245375b0540a3b0ff2e833914462f
Publikováno v:
PLoS ONE, Vol 8, Iss 11, p e78318 (2013)
A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be us
Externí odkaz:
https://doaj.org/article/fae3da15905d47508ef192de2904f7ea
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 7:753-767
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence. 7:872-886
Publikováno v:
IEEE Transactions on Evolutionary Computation. 27:237-250
Publikováno v:
IEEE Transactions on Biomedical Engineering. 70:1137-1149
Deep learning (DL) techniques have been introduced to assist doctors in the interpretation of medical images by detecting image-derived phenotype abnormality. Yet the privacy-preserving policy of medical images disables the effective training of DL m
Autor:
Jing Liang, Xuanxuan Ban, Kunjie Yu, Boyang Qu, Kangjia Qiao, Caitong Yue, Ke Chen, Kay Chen Tan
Publikováno v:
IEEE Transactions on Evolutionary Computation. 27:201-221