Neural network based image and video coding technologies by Chuanmin JIA, Zhenghui ZHAO, Shanshe WANG, Siwei MA

“…Deep neural networks have achieved tremendous success in artificial intelligence,which makes the broad and in-depth research of neural network resurge in recent years.Recently,the neural network based image and video coding has become one of the front-edge topics.A systematic and comprehensive review of neural network based image and video coding approaches based on network structure and coding modules were provided.The development of neural network based image compression,e.g.multi-layer perceptron,random neural network,convolutional neural network,recurrent neural network and generative adversarial network based image compression methods and neural network based video compression tools were introduced respectively.Moreover,the future trends in neural network based compression were also envisioned and discussed.…”
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Relation extraction based on CNN and Bi-LSTM by Xiaobin ZHANG, Fucai CHEN, Ruiyang HUANG

“…Relation extraction aims to identify the entities in the Web text and extract the implicit relationships between entities in the text.Studies have shown that deep neural networks are feasible for relation extraction tasks and are superior to traditional methods.Most of the current relation extraction methods apply convolutional neural network (CNN) and long short-term memory neural network (LSTM) methods.However,CNN just considers the correlation between consecutive words and ignores the correlation between discontinuous words.On the other side,although LSTM takes correlation between long-distance words into account,the extraction features are not sufficiently extracted.In order to solve these problems,a relation extraction method that combining CNN and LSTM was proposed.three methods were used to carry out the experiments,and confirmed the effectiveness of these methods,which had some improvement in F1 score.…”
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Image Quality Assessments by Leveraging Diverse Visual Tasks by Joonhee Lee, Dongwon Park, Se Young Chun

“…While recent advances in deep neural networks (DNNs) have sparked much research on IQA, with the hope for IQA to mimic humans effectively, there has been a lack of systematic and analytical research on understanding what factors humans prioritize during IQA. …”
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Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques by Yu Fujinami-Yokokawa, Nikolas Pontikos, Lizhu Yang, Kazushige Tsunoda, Kazutoshi Yoshitake, Takeshi Iwata, Hiroaki Miyata, Kaoru Fujinami, on behalf of Japan Eye Genetics Consortium

“…It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.…”
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Classification of diabetic retinopathy stages based on neural networks by M. M. Lukashevich, Y. I. Golub

“…Classification problem of diabetic retinopathy stages is described.The architecture of deep neural networks based on VGG16 and VGG19 with the addition of custom layers is proposed. …”
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Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines by Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte

“…Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. …”
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A computational deep learning investigation of animacy perception in the human brain by Stefanie Duyck, Andrea I. Costantino, Stefania Bracci, Hans Op de Beeck

“…While the perception of these objects as animal-like seems obvious to humans, such “Animal bias” is a striking discrepancy between the human brain and deep neural networks (DNNs). We computationally investigated the potential origins of this bias. …”
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Noise-agnostic quantum error mitigation with data augmented neural models by Manwen Liao, Yan Zhu, Giulio Chiribella, Yuxiang Yang

“…Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. …”
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Artificial intelligence in the service of entrepreneurial finance: knowledge structure and the foundational algorithmic paradigm by Robert Kudelić, Tamara Šmaguc, Sherry Robinson

“…The results demonstrate a high representation of artificial neural networks, deep neural networks, and support vector machines across almost all identified topic niches. …”
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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects by Isaac Kofi Nti, Juanita Ahia Quarcoo, Justice Aning, Godfred Kusi Fosu

“…The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA. …”
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Low-Shot Wall Defect Detection for Autonomous Decoration Robots Using Deep Reinforcement Learning by Fanyu Zeng, Xi Cai, Shuzhi Sam Ge

“…Object detection methods based on deep neural networks require a large number of images with the handcrafted bounding box for training. …”
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Preparing Schrödinger Cat States in a Microwave Cavity Using a Neural Network by Hector Hutin, Pavlo Bilous, Chengzhi Ye, Sepideh Abdollahi, Loris Cros, Tom Dvir, Tirth Shah, Yonatan Cohen, Audrey Bienfait, Florian Marquardt, Benjamin Huard

“…Our experimental results demonstrate more generally how deep neural networks and transfer learning can produce efficient simultaneous solutions to a range of quantum control tasks, which will benefit not only state preparation but also parametrized quantum gates.…”
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Multi-feature fusion classification method for communication specific emitter identification by Zunwen HE, Shuai HOU, Wancheng ZHANG, Yan ZHANG

“…A multi-feature fusion classification method based on multi-channel transform projection, integrated deep learning and generative adversarial network (GAN) was proposed for communication specific emitter identification.First, three-dimensional feature images were obtained by performing various transformations, the time and frequency domain projection of the signal was constructed to construct the feature datasets.GAN was used to expand the datasets.Then, a two-stage recognition and classification method based on multi-feature fusion was designed.Deep neural networks were used to learn the three feature datasets, and the initial classification results were obtained.Finally, through fusion and re-learning of the initial classification result, the final classification result was obtained.Based on the measurement and analysis of the actual signals, the experimental results show that the method has higher accuracy than the single feature extraction method.The multipath fading channel has been used to simulate the outdoor propagation environment, and the method has certain generalization performance to adapt to the complex wireless channel environments.…”
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Random Frequency Division Multiplexing by Chanzi Liu, Jianjian Wu, Qingfeng Zhou

“…We take full account of the great power of deep neural networks (DNN) to detect the signal as it is an underdetermined equation. …”
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Transient State Estimation for Power System Based on Deep Transfer Learning by JIAO Hao, ZHAO Jiawei, WEI Lei, ZHU Weiping, MA Zhoujun, ZANG Haixiang

“…Simulations demonstrate that the proposed method exhibits a higher estimation accuracy and computational efficiency than that of deep neural networks without transfer learning，particularly during power system failures.…”
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Generative Elastic Networks (GENs) and Application on Classification of Single-Lead Electrocardiogram by Nan Xiao, Kun Zhao, Hao Zhang

“…Deep neural networks have achieved significant success in various complex machine learning problems. …”
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Detection of OSA Through the Application of Deep Learning on Polysomnography Data by Hasan Ulutas, Recep Sinan Arslan, Muhammet Emin Sahin, Halil Ibrahim Cosar, Cagri Arisoy, Ahmet Sertol Koksal, Mehmet Bakir, Bulent Ciftci

“…The proposed methodology focusses on the use of deep neural networks (DNNs) to enhance the accuracy and reliability of sleep apnea detection. …”
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Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events by Yonghun Song, Inyeol Yun, Sandra Giovanoli, Chris Awai Easthope, Yoonyoung Chung

“…The proposed model integrates multiple deep neural networks based on multi-modal acoustic features of throat-related events to enhance robustness and accuracy of classification. …”
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Recovering manifold representations via unsupervised meta-learning by Yunye Gong, Jiachen Yao, Ruyi Lian, Xiao Lin, Chao Chen, Ajay Divakaran, Yi Yao

“…Manifold representation learning holds great promise for theoretical understanding and characterization of deep neural networks' behaviors through the lens of geometries. …”
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Research on deep reinforcement learning in Internet of vehicles edge computing based on Quasi-Newton method by ZHANG Jianwu, LU Zetao, ZHANG Qianhua, ZHAN Ming

“…The algorithm was designed by initially incorporating batch normalization techniques to optimize the training process of deep neural networks. Subsequently, optimization was performed using the Quasi-Newton method to effectively approximate the optimal solution. …”
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