Results 11 to 20 of about 1,476,903 (264)
Recurrent neural network wave functions [PDF]
Physical Review Research, 2020 A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable likelihood estimate with stable training paradigms, a combination that has ...
Mohamed Hibat-Allah +4 more
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PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical context, where the visual dynamics are believed to have modular structures that can be learned with compositional subsystems.
Yunbo Wang +6 more
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Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network [PDF]
Physica A: Statistical Mechanics and its Applications, 2018 Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blogs, and implementation guides.
A. Sherstinsky
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Temporal-Kernel Recurrent Neural Networks [PDF]
Neural Networks, 2010 A Recurrent Neural Network (RNN) is a powerful connectionist model that can be applied to many challenging sequential problems, including problems that naturally arise in language and speech. However, RNNs are extremely hard to train on problems that have long-term dependencies, where it is necessary to remember events for many timesteps before using ...
Sutskever, Ilya, Hinton, Geoffrey
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Minute-wise frost prediction: An approach of recurrent neural networks
Array, 2022 Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed.
Ian Zhou +3 more
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SummaRuNNer: A Recurrent Neural Network Based Sequence Model for Extractive Summarization of Documents [PDF]
AAAI Conference on Artificial Intelligence, 2016 We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
Ramesh Nallapati +2 more
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A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction [PDF]
International Joint Conference on Artificial Intelligence, 2017 The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the
Yao Qin +5 more
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Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting [PDF]
International Joint Conference on Artificial Intelligence, 2017 Timely accurate traffic forecast is crucial for urban traffic control and guidance. Due to the high nonlinearity and complexity of traffic flow, traditional methods cannot satisfy the requirements of mid-and-long term prediction tasks and often neglect ...
Ting Yu, Haoteng Yin, Zhanxing Zhu
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Speech Command Recognition using Artificial Neural Networks
JOIV: International Journal on Informatics Visualization, 2020 Speech is one of the most effective way for human and machine to interact. This project aims to build Speech Command Recognition System that is capable of predicting the predefined speech commands. Dataset provided by Google’s TensorFlow and AIY teams is
Sushan Poudel, Dr. R Anuradha
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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns.
Shuai Li +4 more
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