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", volume ="390", year = "2022", doi = "https://doi.org/10.1016/j.cma.2021.114476 ", pages = "114476 ", 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 data The files replace version 1 whose zip was corrupted
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...
This third edition essentially compares with the 2nd one, but has been improved by correction of err...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined m...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
The artifact is intended for a "functional/available" badge for ICSE 2023 accepted paper: "Decomposi...
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are in...
1. Error surface of recurrent neural networks. 2. Single-channel blind separation using pseudo-stere...
Simulation data, python code and figures used in the publicationKeup, Kühn, Dahmen, Helias (2021) Tr...
Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), Janua...
Code for our publication "Designing Interpretable Recurrent Neural Networks for Video Reconstruction...
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...
This third edition essentially compares with the 2nd one, but has been improved by correction of err...
Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction ...
peer reviewedArtificial Neural Networks (NNWs) are appealing functions to substitute high dimensiona...
Data related to the publication (we would be grateful if you could cite the paper in the case in whi...
Data for reproducing results with Bayesian decoders (MLE and Bayesian with memory) reported in the a...
These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined m...
de Raedt L, Hammer B, Hitzler P, Maass W, eds. Recurrent Neural Networks - Models, Capacities, and A...
The artifact is intended for a "functional/available" badge for ICSE 2023 accepted paper: "Decomposi...
Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are in...
1. Error surface of recurrent neural networks. 2. Single-channel blind separation using pseudo-stere...
Simulation data, python code and figures used in the publicationKeup, Kühn, Dahmen, Helias (2021) Tr...
Presented at the Georgia Tech Career, Research, and Innovation Development Conference (CRIDC), Janua...
Code for our publication "Designing Interpretable Recurrent Neural Networks for Video Reconstruction...
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...
This third edition essentially compares with the 2nd one, but has been improved by correction of err...