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Robust Convolutional Neural Networks under Adversarial Noise
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". In this work, we propose a new fe…
Convolutional Clustering for Unsupervised Learning
Aysegul Dundar, Jonghoon Jin, Eugenio Culurciello
The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on…
Recurrent Neural Networks Hardware Implementation on FPGA
Andre Xian Ming Chang, Berin Martini, Eugenio Culurciello
Recurrent Neural Networks (RNNs) have the ability to retain memory and learn data sequences. Due to the recurrent nature of RNNs, it is sometimes hard to parallelize all its comput…