Search Results - Computer Learning and Pattern Recognition::Neural Networks*

Complex-Valued Neural Networks: A Comprehensive Survey by Lee, ChiYan, Hasegawa, Hideyuki, Gao, Shangce

“…Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared to their real counter-parts in speech enhancement, image and signal processing…”
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Deep Learning Framework for Classification of Emoji Based Sentiments by Parveen Shaikh, Nighat, Hussain Mahar, Mumtaz

“…Recent patterns of human sentiments are highly influenced by emoji based sentiments (EBS…”
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Automatic Recognition of Speed Limits on Speed-Limit Signs by Using Machine Learning by Miyata, Shigeharu

“… This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP…”
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A survey of robust adversarial training in pattern recognition: Fundamental, theory, and methodologies by Qian, Zhuang, Huang, Kaizhu, Wang, Qiu-Feng, Zhang, Xu-Yao

“… Deep neural networks have achieved remarkable success in machine learning, computer vision, and pattern recognition in the last few decades…”
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A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition by Zhang, Yong, Li, Peng, Jin, Yingyezhe, Choe, Yoonsuck

“… With the proposed online learning, the LSM extracts information from input patterns on the fly without needing intermediate data storage as required in offline learning methods such as ridge regression…”
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Data-driven emergence of convolutional structure in neural networks by Ingrosso, Alessandro, Goldt, Sebastian

“…Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits…”
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Grandmaster level in StarCraft II using multi-agent reinforcement learning by Vinyals, Oriol, Babuschkin, Igor, Czarnecki, Wojciech M., Mathieu, Michaël, Dudzik, Andrew, Chung, Junyoung, Choi, David H., Powell, Richard, Ewalds, Timo, Georgiev, Petko, Oh, Junhyuk, Horgan, Dan, Kroiss, Manuel, Danihelka, Ivo, Huang, Aja, Sifre, Laurent, Cai, Trevor, Agapiou, John P., Jaderberg, Max, Vezhnevets, Alexander S., Leblond, Rémi, Pohlen, Tobias, Dalibard, Valentin, Budden, David, Sulsky, Yury, Molloy, James, Paine, Tom L., Gulcehre, Caglar, Wang, Ziyu, Pfaff, Tobias, Wu, Yuhuai, Ring, Roman, Yogatama, Dani, Wünsch, Dario, McKinney, Katrina, Smith, Oliver, Schaul, Tom, Lillicrap, Timothy, Kavukcuoglu, Koray, Hassabis, Demis, Apps, Chris, Silver, David

“…Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal,…”
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Hands-on Machine Learning with Python - Implement Neural Network Solutions with Scikit-Learn and PyTorch by Ashwin Pajankar, Aditya Joshi

“…Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning…”
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Navegación autónoma del Robot KUKA Youbot con señalización basada en técnicas de detección de objetos y redes neuronales profundas by Encalada, Patricio, Gordón, Carlos, Lema, Henry, León, Diego, Chicaiza, Dennis

“… los vehículos comerciales en industriales. En el texto Autonomous robot navigation based on pattern recognition techniques and artificial neural networks, de Quiñonez et al…”
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Practical emotional neural networks by Lotfi, Ehsan, Akbarzadeh-T., M.-R.

“…In this paper, we propose a limbic-based artificial emotional neural network (LiAENN) for a pattern recognition problem…”
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Deep learning in neural networks: An overview by Schmidhuber, Jürgen

“…In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning…”
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Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks by Pichler, Maximilian, Boreux, Virginie, Klein, Alexandra‐Maria, Schleuning, Matthias, Hartig, Florian, Carvalheiro, Luisa

“… Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning…”
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A New Method of Mixed Gas Identification Based on a Convolutional Neural Network for Time Series Classification by Han, Lu, Yu, Chongchong, Xiao, Kaitai, Zhao, Xia

“… In view of the superiority of convolutional neural networks in the field of computer vision, we applied the concept to the classification of five mixed gas time series data collected by an array…”
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Deep Neural Message Passing With Hierarchical Layer Aggregation and Neighbor Normalization by Fan, Xiaolong, Gong, Maoguo, Tang, Zedong, Wu, Yue

“…As a unified framework for graph neural networks, message passing-based neural network (MPNN…”
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Noise Immunity and Robustness Study of Image Recognition Using a Convolutional Neural Network by Ziyadinov, Vadim, Tereshonok, Maxim

“… We also examined the noise immunity of convolutional neural networks and estimated the influence of uncertainty in the training and testing datasets on recognition probability…”
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Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks by Yin, Shi, Peng, Qinmu, Li, Hongming, Zhang, Zhengqiang, You, Xinge, Fischer, Katherine, Furth, Susan L., Tasian, Gregory E., Fan, Yong

“…•A fully automatic segment method for clinical ultrasound kidney images.•End-to-end learning of boundary detection and pixelwise classification networks…”
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Pattern recognition in living cells through the lens of machine learning by Britto Bisso, Frank, Aguilar, Rodrigo, Shree, Durga, Zhu, Yinan, Espinoza, Mijael, Diaz, Benjamin, Cuba Samaniego, Christian

“…At a coarse level, pattern recognition within cells involves sensing of environmental signals by surface receptors, and activating downstream signalling pathways that ultimately drive a transcriptome…”
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Hybrid methods in pattern recognition (Series in machine perception and artificial intelligence - vol. 47) by Bunke, H, Kandel, A

“…The field of pattern recognition has seen enormous progress since its beginnings almost 50 years ago…”
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A MATLAB toolbox for Self Organizing Maps and supervised neural network learning strategies by Ballabio, Davide, Vasighi, Mahdi

“…Kohonen maps and Counterpropagation Neural Networks are two of the most popular learning strategies based on Artificial Neural Networks. Kohonen Maps…”
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Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data by Ye, Fei

“… (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO…”
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