no code implementations • 19 Nov 2015 • Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
Recent studies have shown that Convolutional Neural Networks (CNNs) are vulnerable to a small perturbation of input called "adversarial examples".
no code implementations • 19 Nov 2015 • Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
In this work, we propose to train a deep convolutional network based on an enhanced version of the k-means clustering algorithm, which reduces the number of correlated parameters in the form of similar filters, and thus increases test categorization accuracy.
Ranked #69 on Image Classification on MNIST
2 code implementations • 17 Dec 2014 • Jonghoon Jin, Aysegul Dundar, Eugenio Culurciello
We present flattened convolutional neural networks that are designed for fast feedforward execution.
1 code implementation • 1 Jun 2013 • Eugenio Culurciello, Jonghoon Jin, Aysegul Dundar, Jordan Bates
On the other hand, in unsupervised learning, one cannot rely on back-propagation techniques to learn the connections between layers.