Deep convolutional neural networks for image classification: A comprehensive review
Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …
1980s. However, despite a few scattered applications, they were dormant until the mid …
Machine learning paradigms for speech recognition: An overview
L Deng, X Li - IEEE Transactions on Audio, Speech, and …, 2013 - ieeexplore.ieee.org
Automatic Speech Recognition (ASR) has historically been a driving force behind many
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
machine learning (ML) techniques, including the ubiquitously used hidden Markov model …
Test-time training with masked autoencoders
Test-time training adapts to a new test distribution on the fly by optimizing a model for each
test input using self-supervision. In this paper, we use masked autoencoders for this one …
test input using self-supervision. In this paper, we use masked autoencoders for this one …
[HTML][HTML] A survey on semi-supervised learning
JE Van Engelen, HH Hoos - Machine learning, 2020 - Springer
Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …
EEG-based emotion recognition using regularized graph neural networks
Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
EEG emotion recognition using dynamical graph convolutional neural networks
In this paper, a multichannel EEG emotion recognition method based on a novel dynamical
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
Virtual adversarial training: a regularization method for supervised and semi-supervised learning
We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …
Adversarial training methods for semi-supervised text classification
Adversarial training provides a means of regularizing supervised learning algorithms while
virtual adversarial training is able to extend supervised learning algorithms to the semi …
virtual adversarial training is able to extend supervised learning algorithms to the semi …
Multisource transfer learning for cross-subject EEG emotion recognition
Electroencephalogram (EEG) has been widely used in emotion recognition due to its high
temporal resolution and reliability. Since the individual differences of EEG are large, the …
temporal resolution and reliability. Since the individual differences of EEG are large, the …
DC programming and DCA: thirty years of developments
HA Le Thi, T Pham Dinh - Mathematical Programming, 2018 - Springer
The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …