Speech recognition using deep neural networks: A systematic review
Over the past decades, a tremendous amount of research has been done on the use of
machine learning for speech processing applications, especially speech recognition …
machine learning for speech processing applications, especially speech recognition …
[HTML][HTML] Deep learning in medical ultrasound analysis: a review
Ultrasound (US) has become one of the most commonly performed imaging modalities in
clinical practice. It is a rapidly evolving technology with certain advantages and with unique …
clinical practice. It is a rapidly evolving technology with certain advantages and with unique …
[HTML][HTML] Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing
Reservoir computing is a highly efficient network for processing temporal signals due to its
low training cost compared to standard recurrent neural networks, and generating rich …
low training cost compared to standard recurrent neural networks, and generating rich …
Software vulnerability detection using deep neural networks: a survey
The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …
important concern in the software industry and in the field of cybersecurity, suggesting that …
Multimodal intelligence: Representation learning, information fusion, and applications
Deep learning methods haverevolutionized speech recognition, image recognition, and
natural language processing since 2010. Each of these tasks involves a single modality in …
natural language processing since 2010. Each of these tasks involves a single modality in …
Power-efficient neural network with artificial dendrites
In the nervous system, dendrites, branches of neurons that transmit signals between
synapses and soma, play a critical role in processing functions, such as nonlinear …
synapses and soma, play a critical role in processing functions, such as nonlinear …
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
In recent years, intelligent fault diagnosis algorithms using machine learning technique have
achieved much success. However, due to the fact that in real world industrial applications …
achieved much success. However, due to the fact that in real world industrial applications …
Efficient processing of deep neural networks: A tutorial and survey
Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI)
applications including computer vision, speech recognition, and robotics. While DNNs …
applications including computer vision, speech recognition, and robotics. While DNNs …
Accelerating federated learning via momentum gradient descent
Federated learning (FL) provides a communication-efficient approach to solve machine
learning problems concerning distributed data, without sending raw data to a central server …
learning problems concerning distributed data, without sending raw data to a central server …
From Eliza to XiaoIce: challenges and opportunities with social chatbots
Conversational systems have come a long way since their inception in the 1960s. After
decades of research and development, we have seen progress from Eliza and Parry in the …
decades of research and development, we have seen progress from Eliza and Parry in the …