Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability

2001

350 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a4354936e35b5a7bdc3ad7222476e5042ad6159a

Citations in the corpus

• Deep learning in agriculture: A survey (2018, methodology): “From the rest, 14 papers developed their own CNN models, 2 papers adopted a first-order Differential Recurrent Neural Networks (DRNN) model, 5 papers preferred to use a Long Short-Term Memory (LSTM) model (Gers, Schmidhuber, & Cummins, 2000), one paper used deep belief networks (DBN) and one paper e”
• Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “To this end, recurrent neural networks [67], which are ANN architectures that allow neuron connections from a neuron in one layer to neu-”
• Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “To this end, recurrent neural networks [84] , which are ANN architectures that allow neuron connections from a neuron in one layer to neurons in previous layers, have been introduced, as shown in Fig.”
• Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions (2021, background): “lar neural network, which employs sequential or timeseries data and feeds the output from the previous step as input to the current stage [28] [74].”
• A review of the use of convolutional neural networks in agriculture (2018, unknown): “The study did not consider/include more advanced and complex models such as Recurrent Neural Networks (RNN; Mandic and Chambers, 2001) or Long Short-Term Memory (LSTM) architectures (Gers et al ., 2000).”
• Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modeling (2019, background): “RNN is designed to learn sequential and time-varying patterns for regression problems [42–45], while CNN is known for its capability in classification of data with grid-like topology (e.g., 1D sequences and 2D images) [46–48].”
• Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining (2018, methodology): “It is obvious that Iter cannot compete with all RNN structures, and is not better than SCAN, as it leaves out information of previous stages.”
• FS-Net: A Flow Sequence Network For Encrypted Traffic Classification (2019, background): “The Recurrent Neural Network (RNN) [27] is one of the most popular neural networks to model sequences.”
• AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems (2022, background): “Multi-Layer Perceptron (MLP) [67], Convolutional Neural Networks (CNN or ConvNet) [53], Recurrent Neural Networks (RNN) [24] [57], and their variants can be used to build the deep discriminative learning models to solve the relevant real-world”