Results 21 to 30 of about 418,704 (219)

Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization [PDF]

, 2020
Artificial intelligence (AI) such as deep learning has achieved significant success in a variety of application domains. Several visualization techniques have been proposed for understanding the overall behavior of the neural network defined by deep ...
Fukusato, Tsukasa, Igarashi, Takeo, Kato, Jun, Zhao, Chunqi   +3 more
core   +1 more source

On the similarities of representations in artificial and brain neural networks for speech recognition

Frontiers in Computational Neuroscience, 2022
IntroductionIn recent years, machines powered by deep learning have achieved near-human levels of performance in speech recognition. The fields of artificial intelligence and cognitive neuroscience have finally reached a similar level of performance ...
Cai Wingfield, Chao Zhang, Barry Devereux, Elisabeth Fonteneau, Andrew Thwaites, Xunying Liu, Phil Woodland, William Marslen-Wilson, Li Su, Li Su   +9 more
doaj   +1 more source

An Introductory Review of Deep Learning for Prediction Models With Big Data

Frontiers in Artificial Intelligence, 2020
Deep learning models stand for a new learning paradigm in artificial intelligence (AI) and machine learning. Recent breakthrough results in image analysis and speech recognition have generated a massive interest in this field because also applications in
Frank Emmert-Streib, Frank Emmert-Streib, Zhen Yang, Han Feng, Han Feng, Shailesh Tripathi, Shailesh Tripathi, Matthias Dehmer, Matthias Dehmer, Matthias Dehmer   +9 more
doaj   +1 more source

Decorrelation-Based Deep Learning for Bias Mitigation

Future Internet, 2022
Although deep learning has proven to be tremendously successful, the main issue is the dependency of its performance on the quality and quantity of training datasets. Since the quality of data can be affected by biases, a novel deep learning method based
Pranita Patil, Kevin Purcell
doaj   +1 more source

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review

Tomography, 2023
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess risk ...
Maham Siddique, Michael Liu, Phuong Duong, Sachin Jambawalikar, Richard Ha   +4 more
doaj   +1 more source

Blood Cell Classification Using the Hough Transform and Convolutional Neural Networks [PDF]

, 2018
https://doi.org/10.1007/978-3-319-77712-2_62The detection of red blood cells in blood samples can be crucial for the disease detection in its early stages.
Luque-Baena, Rafael Marcos, López-Rubio, Ezequiel, Molina-Cabello, Miguel Ángel, Rodríguez-Espinosa, María Jesús, Thurnhofer-Hemsi, Karl   +4 more
core   +1 more source

Flood susceptibility mapping in Brahmaputra floodplain of Bangladesh using deep boost, deep learning neural network, and artificial neural network

Geocarto International, 2021
Floods are considered one of the most destructive natural hydro-meteorological disasters across the world.
Naser Ahmed, Muhammad Al-Amin Hoque, Alireza Arabameri, Subodh Chandra Pal, Rabin Chakrabortty, Jesmin Jui   +5 more
openaire   +1 more source

Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)

Frontiers in Neuroscience, 2020
A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, addresses the similarities between learning dynamics employed in deep artificial
Jacques Kaiser, Hesham Mostafa, Emre Neftci, Emre Neftci   +3 more
doaj   +1 more source

Deep neural networks for understanding and diagnosing partial discharge data [PDF]

, 2015
Artificial neural networks have been investigated for many years as a technique for automated diagnosis of defects causing partial discharge (PD). While good levels of accuracy have been reported, disadvantages include the difficulty of explaining ...
Catterson, V. M., Sheng, B.
core   +1 more source

Cancer diagnosis using deep learning: A bibliographic review [PDF]

, 2019
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the ...
Ayub, Afsheen, Elahi, Hassan, Frezza, Fabrizio, Munir, Khushboo, Rizzi, Antonello   +4 more
core   +1 more source