Automated sleep scoring: A review of the latest approaches
Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …
human expert, according to official standards. It could appear then a suitable task for modern …
An intrusion detection system using a deep neural network with gated recurrent units
C Xu, J Shen, X Du, F Zhang - IEEE Access, 2018 - ieeexplore.ieee.org
To improve the performance of network intrusion detection systems (IDS), we applied deep
learning theory to intrusion detection and developed a deep network model with automatic …
learning theory to intrusion detection and developed a deep network model with automatic …
Probabilistic backpropagation for scalable learning of bayesian neural networks
JM Hernández-Lobato… - … conference on machine …, 2015 - proceedings.mlr.press
Large multilayer neural networks trained with backpropagation have recently achieved state-
of-the-art results in a wide range of problems. However, using backprop for neural net …
of-the-art results in a wide range of problems. However, using backprop for neural net …
Deepsim: deep learning code functional similarity
G Zhao, J Huang - Proceedings of the 2018 26th ACM joint meeting on …, 2018 - dl.acm.org
Measuring code similarity is fundamental for many software engineering tasks, eg, code
search, refactoring and reuse. However, most existing techniques focus on code syntactical …
search, refactoring and reuse. However, most existing techniques focus on code syntactical …
Visual alignment constraint for continuous sign language recognition
Abstract Vision-based Continuous Sign Language Recognition (CSLR) aims to recognize
unsegmented signs from image streams. Overfitting is one of the most critical problems in …
unsegmented signs from image streams. Overfitting is one of the most critical problems in …
Very deep convolutional neural networks for raw waveforms
Learning acoustic models directly from the raw waveform data with minimal processing is
challenging. Current waveform-based models have generally used very few (~ 2) …
challenging. Current waveform-based models have generally used very few (~ 2) …
Speaker gender recognition based on deep neural networks and ResNet50
AA Alnuaim, M Zakariah, C Shashidhar… - Wireless …, 2022 - Wiley Online Library
Several speaker recognition algorithms failed to get the best results because of the wildly
varying datasets and feature sets for classification. Gender information helps reduce this …
varying datasets and feature sets for classification. Gender information helps reduce this …
Who is real bob? adversarial attacks on speaker recognition systems
Speaker recognition (SR) is widely used in our daily life as a biometric authentication or
identification mechanism. The popularity of SR brings in serious security concerns, as …
identification mechanism. The popularity of SR brings in serious security concerns, as …
An Always-On 3.8 J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS
The trend of pushing inference from cloud to edge due to concerns of latency, bandwidth,
and privacy has created demand for energy-efficient neural network hardware. This paper …
and privacy has created demand for energy-efficient neural network hardware. This paper …
Long short-term memory recurrent neural network for automatic speech recognition
Automatic speech recognition (ASR) is one of the most demanding tasks in natural language
processing owing to its complexity. Recently, deep learning approaches have been …
processing owing to its complexity. Recently, deep learning approaches have been …