Search Results - "IEEE transaction on neural networks and learning systems"

A Comprehensive Survey on Graph Neural Networks by Wu, Zonghan, Pan, Shirui, Chen, Fengwen, Long, Guodong, Zhang, Chengqi, Yu, Philip S.

“…Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and…”
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A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI by Tjoa, Erico, Guan, Cuntai

“…Recently, artificial intelligence and machine learning in general have demonstrated remarkable performances in many tasks, from image processing to natural…”
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Deep Neural Networks and Tabular Data: A Survey by Borisov, Vadim, Leemann, Tobias, Sebler, Kathrin, Haug, Johannes, Pawelczyk, Martin, Kasneci, Gjergji

“…Heterogeneous tabular data are the most commonly used form of data and are essential for numerous critical and computationally demanding applications. On…”
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A Comprehensive Survey on Community Detection With Deep Learning by Su, Xing, Xue, Shan, Liu, Fanzhen, Wu, Jia, Yang, Jian, Zhou, Chuan, Hu, Wenbin, Paris, Cecile, Nepal, Surya, Jin, Di, Sheng, Quan Z., Yu, Philip S.

“…Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other…”
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Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints by Sattler, Felix, Muller, Klaus-Robert, Samek, Wojciech

“…Federated learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints…”
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Attention in Natural Language Processing by Galassi, Andrea, Lippi, Marco, Torroni, Paolo

“…Attention is an increasingly popular mechanism used in a wide range of neural architectures. The mechanism itself has been realized in a variety of formats…”
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Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks by Kwon, O-Yeon, Lee, Min-Ho, Guan, Cuntai, Lee, Seong-Whan

“…For a brain-computer interface (BCI) system, a calibration procedure is required for each individual user before he/she can use the BCI. This procedure…”
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DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data by Dablain, Damien, Krawczyk, Bartosz, Chawla, Nitesh V.

“…Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in…”
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Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing by Hong, Danfeng, Gao, Lianru, Yao, Jing, Yokoya, Naoto, Chanussot, Jocelyn, Heiden, Uta, Zhang, Bing

“…Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet…”
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Inverting the Generator of a Generative Adversarial Network by Creswell, Antonia, Bharath, Anil Anthony

“…Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are…”
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Completely Automated CNN Architecture Design Based on Blocks by Sun, Yanan, Xue, Bing, Zhang, Mengjie, Yen, Gary G.

“…The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive…”
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Dynamically Weighted Balanced Loss: Class Imbalanced Learning and Confidence Calibration of Deep Neural Networks by Fernando, K. Ruwani M., Tsokos, Chris P.

“…Imbalanced class distribution is an inherent problem in many real-world classification tasks where the minority class is the class of interest. Many…”
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Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing by Yang, Shuangming, Wang, Jiang, Deng, Bin, Azghadi, Mostafa Rahimi, Linares-Barranco, Bernabe

“…Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-tolerant on-chip…”
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Active Learning for Deep Visual Tracking by Yuan, Di, Chang, Xiaojun, Liu, Qiao, Yang, Yi, Wang, Dehua, Shu, Minglei, He, Zhenyu, Shi, Guangming

“…Convolutional neural networks (CNNs) have been successfully applied to the single target tracking task in recent years. Generally, training a deep CNN model…”
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Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks by Zhang, Malu, Wang, Jiadong, Wu, Jibin, Belatreche, Ammar, Amornpaisannon, Burin, Zhang, Zhixuan, Miriyala, Venkata Pavan Kumar, Qu, Hong, Chua, Yansong, Carlson, Trevor E., Li, Haizhou

“…Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit information, which are not only biologically realistic but also…”
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Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks by Gysel, Philipp, Pimentel, Jon, Motamedi, Mohammad, Ghiasi, Soheil

“…Convolutional neural networks (CNNs) have led to remarkable progress in a number of key pattern recognition tasks, such as visual scene understanding and…”
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Design and Implementation of Deep Neural Network-Based Control for Automatic Parking Maneuver Process by Chai, Runqi, Tsourdos, Antonios, Savvaris, Al, Chai, Senchun, Xia, Yuanqing, Chen, C. L. Philip

“…This article focuses on the design, test, and validation of a deep neural network (DNN)-based control scheme capable of predicting optimal motion commands for…”
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A Robust Regularization Path Algorithm for \nu -Support Vector Classification by Bin Gu, Sheng, Victor S.

“…The v-support vector classification has the advantage of using a regularization parameter v to control the number of support vectors and margin errors…”
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Open Set Domain Adaptation: Theoretical Bound and Algorithm by Fang, Zhen, Lu, Jie, Liu, Feng, Xuan, Junyu, Zhang, Guangquan

“…The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model's learning performance with an unlabeled…”
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Structural Minimax Probability Machine by Bin Gu, Xingming Sun, Sheng, Victor S.

“…Minimax probability machine (MPM) is an interesting discriminative classifier based on generative prior knowledge. It can directly estimate the probabilistic…”
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