Deep learning for intelligent wireless networks: A comprehensive survey
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 …
complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to …
On the origin of deep learning
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 …
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
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 …
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 …
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
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 …
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
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 …
loss. Raised intravascular pressure is the only factor which can be modified to prevent …
Human-level control through deep reinforcement learning
The theory of reinforcement learning provides a normative account, deeply rooted in
psychological and neuroscientific perspectives on animal behaviour, of how agents may …
psychological and neuroscientific perspectives on animal behaviour, of how agents may …
Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals
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 …
convolutional neural system is described in this study. Schizophrenia is an anomaly in the …
On the expressive power of deep learning: A tensor analysis
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 …
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 …
performance on tasks such as the recognition of handwritten digits or traffic signs. Our …