Výsledky vyhľadávania - "Backpropagation algorithms"

AI Deep Learning Optimization for Compact Dual-Polarized High-Isolation Antenna Using Backpropagation Algorithm Autor Wu, Duo-Long, Hu, Xiao Jian, Chen, Jin Hao, Ye, Liang Hua, Li, Jian-Feng

“…An artificial intelligence deep learning algorithm is proposed to analyze a dual-polarized high-isolation antenna effectively. The method is a building model…”
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Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits Autor Krestinskaya, Olga, Salama, Khaled Nabil, James, Alex Pappachen

“…The on-chip implementation of learning algorithms would speed up the training of neural networks in crossbar arrays. The circuit level design and…”
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Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction Autor Chiroma, Haruna, Abdulkareem, Sameem, Herawan, Tutut

“…•We propose approach for the prediction of the WTI crude oil price.•The values predicted by the proposed method and actual once are statistically equal.•The…”
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An Improved Prediction Algorithm for Noise of Transformer Considering Material Parameter Uncertainty Autor Yang, Fan, Xia, Yisha, Wang, Jiawei, Wang, Pengbo, Jiang, Hui

“…This study proposes a rapid transformer noise prediction method to quantify the propagation of electrical steel material uncertainties in transformer systems…”
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The Symplectic Adjoint Method: Memory-Efficient Backpropagation of Neural-Network-Based Differential Equations Autor Matsubara, Takashi, Miyatake, Yuto, Yaguchi, Takaharu

“…The combination of neural networks and numerical integration can provide highly accurate models of continuous-time dynamical systems and probabilistic…”
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Towards Accurate One-Stage Object Detection With AP-Loss Autor Chen, Kean, Li, Jianguo, Lin, Weiyao, See, John, Wang, Ji, Duan, Lingyu, Chen, Zhibo, He, Changwei, Zou, Junni

“…One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously, with the former suffering much from extreme…”
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Low-Complexity Fiber Nonlinearity Compensation Based on Operator Learning Over 12 075 km Single Model Fiber Autor He, Xingchen, Yan, Lianshan, Jiang, Lin, Yi, Anlin, Yu, Youren, Pu, Zhengyu, Pan, Wei, Luo, Bin

“…Fiber nonlinearity is a significant constraint on the maximum achievable capacity in long-distance fiber optic transmission systems. The fiber nonlinearity…”
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DeepBlue: Advanced convolutional neural network applications for ocean remote sensing Autor Wang, Haoyu, Li, Xiaofeng

“…In the last 40 years, remote sensing technology has evolved, significantly advancing ocean observation and catapulting its data into the big data era. How to…”
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Non-Parallel Voice Conversion Using Variational Autoencoders Conditioned by Phonetic Posteriorgrams and D-Vectors Autor Saito, Yuki, Ijima, Yusuke, Nishida, Kyosuke, Takamichi, Shinnosuke

“…This paper proposes novel frameworks for non-parallel voice conversion (VC) using variational autoencoders (VAEs). Although conventional VAE-based VC models…”
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Improved Computation for Levenberg-Marquardt Training Autor Wilamowski, Bogdan M, Hao Yu

“…The improved computation presented in this paper is aimed to optimize the neural networks learning process using Levenberg-Marquardt (LM) algorithm…”
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Error Minimized Extreme Learning Machine With Growth of Hidden Nodes and Incremental Learning Autor Guorui Feng, Guang-Bin Huang, Qingping Lin, Gay, R.

“…One of the open problems in neural network research is how to automatically determine network architectures for given applications. In this brief, we propose a…”
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Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling Autor Passalis, Nikolaos, Tefas, Anastasios

“…Convolutional neural networks (CNNs) are predominantly used for several challenging computer vision tasks achieving state-of-the-art performance. However, CNNs…”
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An Integrated Deep Learning-Based Data Fusion and Degradation Modeling Method for Improving Prognostics Autor Wang, Di, Liu, Kaibo

“…Accurate prognostics are crucially important to prevent unexpected failures in industrial and service systems. This process aims to monitor the degradation…”
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Supervised Learning in Neural Networks: Feedback-Network-Free Implementation and Biological Plausibility Autor Lin, Feng

“…The well-known backpropagation learning algorithm is probably the most popular learning algorithm in artificial neural networks. It has been widely used in…”
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Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization Autor ZHANG, Min-Ling, ZHOU, Zhi-Hua

“…In multilabel learning, each instance in the training set is associated with a set of labels and the task is to output a label set whose size is unknown a…”
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Training Photonic Mach Zehnder Meshes for Neural Network Acceleration Autor Wolff, Andy, Karanth, Avinash

“…Photonic neural networks enable faster and energy-efficient inferences for deep neural network implementations when compared to electrical counterparts. Prior…”
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Why RELU Units Sometimes Die: Analysis of Single-Unit Error Backpropagation in Neural Networks Autor Douglas, Scott C., Yu, Jiutian

“…Recently, neural networks in machine learning use rectified linear units (ReLUs) in early processing layers for better performance. Training these structures…”
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Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network Autor Panagiotou, Dimitrios K., Dounis, Anastasios I.

“…Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved…”
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Mitigation of Fiber Nonlinearity Using a Digital Coherent Receiver Autor Millar, D S, Makovejs, S, Behrens, C, Hellerbrand, S, Killey, R I, Bayvel, P, Savory, S J

“…Coherent detection with receiver-based DSP has recently enabled the mitigation of fiber nonlinear effects. We investigate the performance benefits available…”
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Accelerating convolutional neural network training using ProMoD backpropagation algorithm Autor Gürhanlı, Ahmet

“…Convolutional neural networks (CNNs) play an important role in image recognition applications. Fast training of image recognition systems is a crucial point,…”
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