Deep learning has made substantial breakthroughs in many fields due to its powerful
automatic representation capabilities. It has been proven that neural architecture design is …

Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Abstract We present Mobile Video Networks (MoViNets), a family of computation and
memory efficient video networks that can operate on streaming video for online inference …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

Conventional neural architecture search (NAS) approaches are usually based on
reinforcement learning or evolutionary strategy, which take more than 1000 GPU hours to …

Deep Learning has enabled remarkable progress over the last years on a variety of tasks,
such as image recognition, speech recognition, and machine translation. One crucial aspect …

Designing convolutional neural networks (CNN) for mobile devices is challenging because
mobile models need to be small and fast, yet still accurate. Although significant efforts have …

In the past decade, advances in deep learning have resulted in breakthroughs in a variety of
areas, including computer vision, natural language understanding, speech recognition, and …