Results 31 to 40 of about 30,985 (264)
Low Precision RNNs: Quantizing RNNs Without Losing Accuracy
CoRR, 2017 Similar to convolution neural networks, recurrent neural networks (RNNs) typically suffer from over-parameterization. Quantizing bit-widths of weights and activations results in runtime efficiency on hardware, yet it often comes at the cost of reduced accuracy.
Supriya Kapur +2 more
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Modelling Time Series Data for Stock Prices Prediction Using Bidirectional Long Short-Term Memory
KnowbaseThe dynamic nature of stock markets, characterized by intricate patterns and sudden fluctuations, poses significant challenges to accurate price prediction. Traditional analytical methods are often unable to capture this complexity. This requires the use
Yenie Syukriyah, Adi Purnama
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CoRR, 2021 Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, precise reasoning for this behavior is still unknown. This paper provides a rigorous explanation of this property in the special case of linear RNNs.
Melikasadat Emami +4 more
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Time-Series Forecasting of the Pazarcık Earthquake Using LSTM, Transformer and RNN Models
Gazi Üniversitesi Fen Bilimleri DergisiThe Earth's internal structure and mitigating seismic hazards are very important for understanding for earthquake prediction and seismic wave analysis.
Seda Şahin , Emine Çankaya
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DB-RNN: An RNN for Precipitation Nowcasting Deblurring
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingPrecipitation nowcasting based on artificial intelligence has garnered widespread attention in the meteorological and computer communities in recent years. While new models are continuously proposed to refresh the forecasting performance, the problem of gradual blurring of forecast maps as the forecast period extends is still serious.
Zhifeng Ma, Hao Zhang 0016, Jie Liu 0001
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W-RNN: News text classification based on a Weighted RNN
CoRR, 2019 7 pages, 10 ...
Dan Wang, Jibing Gong, Yaxi Song
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2017 International Joint Conference on Neural Networks (IJCNN), 2017 In this paper we examine learning methods combining the Random Neural Network, a biologically inspired neural network and the Extreme Learning Machine that achieve state of the art classification performance while requiring much shorter training time.
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Improved Recurrent Neural Network based BP Decoding Algorithm for Polar Codes
Guangtongxin yanjiu, 2022 In recent years, the emerging Deep Learning (DL) technology has made progress in the field of decoding. Current polar code neural network decoder has faster convergence speed and better Bit Error Rate (BER) performance than Belief Propagation (BP ...
Xue-lu DENG, Da-qin PENG
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Information Transmission Strategies for Self‐Organized Robotic Aggregation
Advanced Robotics Research, EarlyView.In this review, we discuss how information transmission influences the neighbor‐based self‐organized aggregation of swarm robots. We focus specifically on local interactions regarding information transfer and categorize previous studies based on the functions of the information exchanged.
Shu Leng +5 more
wiley +1 more source
Neutrosophy-Driven Deep Learning for Predicting Student Performance [PDF]
Neutrosophic Sets and SystemsThis paper proposes a hybrid architecture using several deep learning models in the neutrosophy environment for predicting student learning outcomes. The proposed framework proceeds on deep neural network models with the neutrosophy encoder/decoder.
N.T.K Son, N.T. Thong, N.H. Quynh
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