25 citations · 36 across the 8 of their papers we have counts for
6 papers · 1 filter
Training a Two Layer ReLU Network Analytically
Adrian Barbu
Neural networks are usually trained with different variants of gradient descent based optimization algorithms such as stochastic gradient descent or the Adam optimizer. Recent theo…
Feature Selection with Annealing for Forecasting Financial Time Series
Hakan Pabuccu, Adrian Barbu
Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy or hold strategies so that they may…
A study of local optima for learning feature interactions using neural networks
Yangzi Guo, Adrian Barbu
In many fields such as bioinformatics, high energy physics, power distribution, etc., it is desirable to learn non-linear models where a small number of variables are selected and…
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Gitesh Dawer, Yangzi Guo, Sida Liu +1
Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irre…
Network Pruning via Annealing and Direct Sparsity Control
Yangzi Guo, Yiyuan She, Adrian Barbu
Artificial neural networks (ANNs) especially deep convolutional networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vis…
The Generalization-Stability Tradeoff In Neural Network Pruning
Brian R. Bartoldson, Ari S. Morcos, Adrian Barbu +1
Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting. This motivation is partic…