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4 papers · 2 filters
Embedded Binarized Neural Networks
Bradley McDanel, Surat Teerapittayanon, H. T. Kung
We study embedded Binarized Neural Networks (eBNNs) with the aim of allowing current binarized neural networks (BNNs) in the literature to perform feedforward inference efficiently…
Distributed Deep Neural Networks over the Cloud, the Edge and End Devices
Surat Teerapittayanon, Bradley McDanel, H. T. Kung
We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate…
Adversarial nets with perceptual losses for text-to-image synthesis
Miriam Cha, Youngjune Gwon, H. T. Kung
Recent approaches in generative adversarial networks (GANs) can automatically synthesize realistic images from descriptive text. Despite the overall fair quality, the generated ima…
A Multi-Scale CNN and Curriculum Learning Strategy for Mammogram Classification
William Lotter, Greg Sorensen, David Cox
Screening mammography is an important front-line tool for the early detection of breast cancer, and some 39 million exams are conducted each year in the United States alone. Here,…