Search Results for author:
Found 5 papers, 0 papers with code
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Artificial Neural Networks form the basis of very powerful learning methods.
Network Pruning via Annealing and Direct Sparsity Control
The attractive fact that the network size keeps dropping throughout the iterations makes it suitable for the pruning of any untrained or pre-trained network.
A study of local optima for learning feature interactions using neural networks
To deal with the local minima and for feature selection we propose a node pruning and feature selection algorithm that improves the capability of NNs to find better local minima even when there are irrelevant variables.
Finding Deep Local Optima Using Network Pruning
However, in non-vision sparse datasets, especially with many irrelevant features where a standard neural network would overfit, this might not be the case and there might be many non-equivalent local optima.
Generating Compact Tree Ensembles via Annealing
Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner.
