Journal ArticleDOI
A survey on deep learning and its applications
TLDR
The structural principle, the characteristics, and some kinds of classic models of deep learning, such as stacked auto encoder, deep belief network, deep Boltzmann machine, and convolutional neural network are described.About:
This article is published in Computer Science Review.The article was published on 2021-05-01. It has received 408 citations till now. The article focuses on the topics: Deep belief network & Deep learning.read more
Citations
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Journal ArticleDOI
A Survey on Neural Network Interpretability
TL;DR: A comprehensive review of the neural network interpretability research can be found in this paper, where a novel taxonomy organized along three dimensions: type of engagement (passive vs. active interpretation approaches), the type of explanation, and the focus (from local to global interpretability).
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
TL;DR: In this article, simple atom and bond attributes are used to construct atom-specific feature vectors that take into account the local chemical environment using different neighborhood radii, which significantly enhances model performance.
Posted Content
A Survey of Human-in-the-loop for Machine Learning.
TL;DR: In this article, the authors survey existing works on human-in-the-loop from a data perspective and classify them into three categories with a progressive relationship: (1) the work of improving model performance from data processing, (2) the improvement model performance through interventional model training, and (3) the design of the system independent human in the loop.
Journal ArticleDOI
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review
TL;DR: In this article , a review of the EEG-based emotion recognition methods is presented, including feature extraction, feature selection/reduction, machine learning methods (e.g., k-nearest neighbor), support vector machine, decision tree, artificial neural network, random forest, and naive Bayes) and deep learning methods.
Video Summarization Using Deep Neural Networks: A Survey
Evlampios Apostolidis,Eleni Adamantidou,Alexandros I. Metsai,Vasileios Mezaris,Ioannis Patras +4 more
TL;DR: A comprehensive survey of the deep learning-based methods for video summarization can be found in this article, where the authors provide a taxonomy of the existing algorithms and provide a systematic review of the relevant literature.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.