Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.", journal = "Computer Methods in Applied Mechanics and Engineering", year = "2022", doi = "https://doi.org/####", pages = "####", author = "Wu, Ling and Noels, Ludovic" We would be grateful if you could cite the paper in the case in which you are using the dat
These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined m...
The application of neural networks in the data mining has become wider. Data mining is the search fo...
Modem data analysis often faces high-dimensional data. Nevertheless, most neural network data analys...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
1. Error surface of recurrent neural networks. 2. Single-channel blind separation using pseudo-stere...
Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), Janua...
Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a ...
Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the underlyin...
The paper presents a technique for generating concise neural network models of physical systems. The...
Dataset of the paper:Li, X., Dyck, O.E., Oxley, M.P., Lupini, A.R., McInnes, L., Healy, J., Jesse, S...
These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined m...
The application of neural networks in the data mining has become wider. Data mining is the search fo...
Modem data analysis often faces high-dimensional data. Nevertheless, most neural network data analys...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the conn...
1. Error surface of recurrent neural networks. 2. Single-channel blind separation using pseudo-stere...
Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), Janua...
Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a ...
Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the underlyin...
The paper presents a technique for generating concise neural network models of physical systems. The...
Dataset of the paper:Li, X., Dyck, O.E., Oxley, M.P., Lupini, A.R., McInnes, L., Healy, J., Jesse, S...
These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined m...
The application of neural networks in the data mining has become wider. Data mining is the search fo...
Modem data analysis often faces high-dimensional data. Nevertheless, most neural network data analys...