Recurrent Neural Networks: Design and Applications

By Lakhmi C. Jain, L. Medsker. 1999

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Citations in the corpus

Toward Continual Learning for Conversational Agents (2017, methodology): “loy a neural encoder for state tracking and a neural ranking model for action selection. State Tracking Due to the sequential nature of the conversation, variants of Recurrent Neural Networks (RNNs) (Medsker and Jain, 1999) have been widely adopted for modeling conversation. State tracking models wi”
Dynamic Graph Convolutional Networks (2017, background): “LSTM s are a special kind of Recurrent Neural Networks ( RNN s, [10]), which are able to improve the learning of long short-term dependencies.”
Physics-Informed Multi-LSTM Networks for Metamodeling of Nonlinear Structures (2020, background): “ Thanks to the state-of-the-art advances in articial intelligence (AI), recent studies have shown that deep learning (e.g., convolutional neural network (CNN) [37] and recurrent neural network (RNN) [38,39]) is a promising approach to establish metamodels for fast prediction of time history response”
Optimization of fused deposition modeling process parameters: a review of current research and future prospects (2015, background): “It has the ability to identify complex non-linear relationships between process parameters and output [40, 41].”
Multimodal Language Analysis with Recurrent Multistage Fusion (2018, unknown): “Multimodal Temporal Neural Networks: More recently, with the advent of deep learning, Recurrent Neural Networks (Elman, 1990; Jain and Medsker, 1999) have been used extensively for language and speech based sequence modeling (Zilly et al., 2016; Soltau et al., 2016), sentiment analysis (Socher et…”