Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks
with the price of high computational demands. Inspired by the observation that spatial …

For models trained on-premise but deployed in a distributed fashion across multiple entities,
we demonstrate that minimizing distance correlation between sensitive data such as faces …

This study presents a comprehensive analysis of utilizing TensorFlow Lite on mobile phones
for the on-edge medical diagnosis of lung diseases. This paper focuses on the technical …

Embedding high-dimensional data onto a low-dimensional manifold is of both theoretical
and practical value. In this article, we propose to combine deep neural networks (DNN) with …

Efficient deep learning models, especially optimized for edge devices, benefit from low
inference latency to efficient energy consumption. Two classical techniques for efficient …

Quantization is a technique for creating efficient Deep Neural Networks (DNNs), which
involves performing computations and storing tensors at lower bit-widths than f32 floating …

Resource constraints, workload overheads, lack of trust, and competition hinder the sharing
of raw data across multiple institutions. This leads to a shortage of data for training state-of …

As deep neural networks make their ways into different domains, their compute efficiency is
becoming a first-order constraint. Deep quantization, which reduces the bitwidth of the …

As deep neural networks make their ways into different domains and application, their
compute efficiency is becoming a first-order constraint. Deep quantization, which reduces …

Convolutional neural networks (CNNs) have demonstrated remarkable capability and
scalability in a variety of vision-related tasks. Due to privacy and latency constraints, in some …