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An Evolutionary-Algorithm-Driven Efficient Temporal Convolutional Network for Radar Image Extrapolation. [PDF]

Biomimetics (Basel)
Wei P, Fan C, Wang Y, Li T, Gan J, Hu C, Li Z.   +6 more
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Advanced deep learning techniques for classifying dental conditions using panoramic X-ray images. [PDF]

BMC Oral Health
Golkarieh A, Afjehsoleymani B, Kiashemshaki K, Boroujeni SR.   +3 more
europepmc   +1 more source

Transfer learning with Bayesian optimization for colorectal cancer histopathology classification. [PDF]

BMC Med Imaging
ALGhafri HS, Lim CS.
europepmc   +1 more source

A new framework with convoluted oscillatory neural network for efficient object-based land use and land cover classification on remote sensing images. [PDF]

Front Artif Intell
Chandnani CJ, Kulkarni SC, Amali D GB, Selvaraj R.   +3 more
europepmc   +1 more source

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Biased Dropout and Crossmap Dropout: Learning towards effective Dropout regularization in convolutional neural network

Neural Networks, 2018
Training a deep neural network with a large number of parameters often leads to overfitting problem. Recently, Dropout has been introduced as a simple, yet effective regularization approach to combat overfitting in such models. Although Dropout has shown remarkable results on many deep neural network cases, its actual effect on CNN has not been ...
Alvin Poernomo, Dae-Ki Kang
openaire   +2 more sources

Dropout algorithms for recurrent neural networks

Proceedings of the Annual Conference of the South African Institute of Computer Scientists and Information Technologists, 2018
In the last decade, hardware advancements have allowed for neural networks to become much larger in size. Dropout is a popular deep learning technique which has shown to improve the performance of large neural networks. Recurrent neural networks are powerful networks specialised at solving problems which use time series data. Three different approaches
Nathan Watt, Mathys C. du Plessis
openaire   +1 more source