Deep learning for intelligent wireless networks: A comprehensive survey

Q Mao, F Hu, Q Hao - IEEE Communications Surveys & …, 2018 - ieeexplore.ieee.org
As a promising machine learning tool to handle the accurate pattern recognition from
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …

On the origin of deep learning

H Wang, B Raj - arXiv preprint arXiv:1702.07800, 2017 - arxiv.org
This paper is a review of the evolutionary history of deep learning models. It covers from the
genesis of neural networks when associationism modeling of the brain is studied, to the …

Deep feature based rice leaf disease identification using support vector machine

PK Sethy, NK Barpanda, AK Rath, SK Behera - Computers and Electronics …, 2020 - Elsevier
Features are the vital factor for image classification in the field of machine learning. The
advancement of deep convolutional neural network (CNN) shows the way for identification …

Refining activation downsampling with SoftPool

A Stergiou, R Poppe… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Abstract Convolutional Neural Networks (CNNs) use pooling to decrease the size of
activation maps. This process is crucial to increase the receptive fields and to reduce …

A deep learning approach for Parkinson's disease diagnosis from EEG signals

SL Oh, Y Hagiwara, U Raghavendra, R Yuvaraj… - Neural Computing and …, 2020 - Springer
An automated detection system for Parkinson's disease (PD) employing the convolutional
neural network (CNN) is proposed in this study. PD is characterized by the gradual …

Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

U Raghavendra, H Fujita, SV Bhandary, A Gudigar… - Information …, 2018 - Elsevier
Glaucoma progressively affects the optic nerve and may cause partial or complete vision
loss. Raised intravascular pressure is the only factor which can be modified to prevent …

Human-level control through deep reinforcement learning

V Mnih, K Kavukcuoglu, D Silver, AA Rusu, J Veness… - nature, 2015 - nature.com
The theory of reinforcement learning provides a normative account, deeply rooted in
psychological and neuroscientific perspectives on animal behaviour, of how agents may …

Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals

SL Oh, J Vicnesh, EJ Ciaccio, R Yuvaraj, UR Acharya - Applied Sciences, 2019 - mdpi.com
A computerized detection system for the diagnosis of Schizophrenia (SZ) using a
convolutional neural system is described in this study. Schizophrenia is an anomaly in the …

On the expressive power of deep learning: A tensor analysis

N Cohen, O Sharir, A Shashua - Conference on learning …, 2016 - proceedings.mlr.press
It has long been conjectured that hypotheses spaces suitable for data that is compositional
in nature, such as text or images, may be more efficiently represented with deep hierarchical …

Multi-column deep neural networks for image classification

D Ciregan, U Meier… - 2012 IEEE conference on …, 2012 - ieeexplore.ieee.org
Traditional methods of computer vision and machine learning cannot match human
performance on tasks such as the recognition of handwritten digits or traffic signs. Our …