234 citations · 548 across the 25 of their papers we have counts for
4 papers · 1 filter
Are screening methods useful in feature selection? An empirical study
Mingyuan Wang, Adrian Barbu
Filter or screening methods are often used as a preprocessing step for reducing the number of variables used by a learning algorithm in obtaining a classification or regression mod…
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Brian Bartoldson, Adrian Barbu, Gordon Erlebacher
Artificial neural networks (ANNs) may not be worth their computational/memory costs when used in mobile phones or embedded devices. Parameter-pruning algorithms combat these costs,…
A Novel Framework for Online Supervised Learning with Feature Selection
Lizhe Sun, Mingyuan Wang, Siquan Zhu +1
Current online learning methods suffer issues such as lower convergence rates and limited capability to select important features compared to their offline counterparts. In this pa…
Random Hinge Forest for Differentiable Learning
Nathan Lay, Adam P. Harrison, Sharon Schreiber +2
We propose random hinge forests, a simple, efficient, and novel variant of decision forests. Importantly, random hinge forests can be readily incorporated as a general component wi…