Google Inc.

Cynosure, Inc.

Bo Chen

Rafael Armando Sierra

Allan Cameron

Kevin Young

Adrian Mark West

Elizabeth Kneen Winter

James Henry Boll

Quoc V Le

Ruoming Pang

Vijay Vasudevan

Mingxing Tan

Yang Song

Charles Joseph Rosenberg

Dmitry Kalenichenko

Andrew Gerald Howard

Ariel Gordon

Menglong Zhu

Elad Eban

Andrew Yan-Tak Ng

Ofir Nachum

James Boll

Marco Andreetto

Tobias Christoph Weyand

Weijun Wang

Elizabeth Kneen

In accordance with various aspects of the present teachings, systems and methods for applying treatment energy, e.g., electromagnetic radiation such as laser radiation in the visible and near infrared wavelengths, to body areas having bulges and fat deposits, loose skin, pain, acne and/or wounds. In some aspects, the systems and methods can enable relatively lengthy treatments to be performed by having the practitioner set-up and/or start the treatment, thereafter allowing the treatment to proceed safely and effectively without the continued presence of the practitioner.

System and methods of unattended treatment

The present disclosure is directed to an automated neural architecture search approach for designing new neural network architectures such as, for example, resource-constrained mobile CNN models. In particular, the present disclosure provides systems and methods to perform neural architecture search using a novel factorized hierarchical search space that permits layer diversity throughout the network, thereby striking the right balance between flexibility and search space size. The resulting neural architectures are able to be run relatively faster and using relatively fewer computing resources (e.g., less processing power, less memory usage, less power consumption, etc.), all while remaining competitive with or even exceeding the performance (e.g., accuracy) of current state-of-the-art mobile-optimized models.

Neural architecture search with factorized hierarchical search space

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks. In one aspect, a system includes a neural network shrinking engine that is configured to receive a neural network being trained and generate a reduced neural network by a shrinking process. The shrinking process includes training the neural network based on a shrinking engine loss function that includes terms penalizing active neurons of the neural network and removing inactive neurons from the neural network. The system includes a neural network expansion engine that is configured to receive the neural network being trained and generate an expanded neural network by an expansion process including adding new neurons to the neural network and training the neural network based on an expanding engine loss function. The system includes a training subsystem that generates reduced neural networks and expanded neural networks.

Learning neural network structure

The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed 'MobileNets' for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.

Efficient convolutional neural networks and techniques to reduce associated computational costs

Systems and methods of unattended treatment

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for creating an image similarity model. In one aspect, a method includes obtaining feature vectors for images in a set of images, and determining first similarity measures for unlabeled images relative to a reference image. The first similarity measures are independent of first similarity feedback between the unlabeled images and the reference image. The unlabeled images are ranked based on the first similarity measures, and a weighted feature vector is generated based, in part, on the ranking. Second similarity measures are determined, independent of second similarity feedback, for labeled images and a second reference image. The labeled images are ranked based on the second similarity measures. The weighted feature vector is adjusted based, in part, on a comparison of the ranking to a second ranking of the labeled images that is based on the second similarity feedback.

Evaluating image similarity

Growing community of inventors

Pasadena, CA, United States of America

Bo Chen