The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.©2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be o...
Recurrent neural network processing of regular languages is reasonably well understood. Recent work ...
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural...
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. Accor...
The long short-term memory (LSTM) is not the only neural network which learns a context sensitive la...
Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languag...
Long short-term memory(LSTM) units on sequence-based models are being used in translation, question-...
Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research ...
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is designed to handle...
Ebru Arısoy (MEF Author)Long Short-Term Memory (LSTM) neural networks are recurrent neural networks ...
There is a need to clarify the relationship between traditional symbolic computation and neural netw...
In recent years it has been shown that first order recurrent neural networks trained by gradient-des...
Abstract. We have investigated two specific network types in the class of dynamic neural networks: L...
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a d...
Abstract — In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a mod...
Two classes of recurrent neural network models are compared in this report, simple recurrent network...
Recurrent neural network processing of regular languages is reasonably well understood. Recent work ...
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural...
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. Accor...
The long short-term memory (LSTM) is not the only neural network which learns a context sensitive la...
Continuous-valued recurrent neural networks can learn mechanisms for processing context-free languag...
Long short-term memory(LSTM) units on sequence-based models are being used in translation, question-...
Applying Artificial Neural Networks (ANNs) to language learning has been an active area of research ...
Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) that is designed to handle...
Ebru Arısoy (MEF Author)Long Short-Term Memory (LSTM) neural networks are recurrent neural networks ...
There is a need to clarify the relationship between traditional symbolic computation and neural netw...
In recent years it has been shown that first order recurrent neural networks trained by gradient-des...
Abstract. We have investigated two specific network types in the class of dynamic neural networks: L...
Recurrent Neural Networks (RNNs) are theoretically Turing-complete and established themselves as a d...
Abstract — In this paper, we present bidirectional Long Short Term Memory (LSTM) networks, and a mod...
Two classes of recurrent neural network models are compared in this report, simple recurrent network...
Recurrent neural network processing of regular languages is reasonably well understood. Recent work ...
Long Short-Term Memory (LSTM) and Transformers are two popular neural architectures used for natural...
Long short-term memory (LSTM) has transformed both machine learning and neurocomputing fields. Accor...