Leland Stanford Junior University

Google Inc.

Nec Laboratories America, Inc.

Fei-Fei Li

Jia H Deng

Jonathan Krause

Alexander C Berg

Jonathan J Huang

Jonathon Shlens

Kai Hung Yu

Barret Zoph

Wei Hua

Yuanqing Lin

Silvio Savarese

Kevin Patrick Murphy

Yuke Zhu

Jia Li

Olga Russakovsky

Ajay U Mandlekar

Animesh Garg

Methods and systems to remotely operate robotic devices are provided. A number of embodiments allow users to remotely operate robotic devices using generalized consumer devices (e.g., cell phones). Additional embodiments provide for a platform to allow communication between consumer devices and the robotic devices. Further embodiments allow for training robotic devices to operate autonomously by training the robotic device with machine learning algorithms using data collected from scalable methods of controlling robotic devices.

Methods and systems to remotely operate robotic devices

A method for determining an architecture for a task neural network configured to perform a particular machine learning task is described. The method includes obtaining data specifying a current set of candidate architectures for the task neural network; for each candidate architecture in the current set: processing the data specifying the candidate architecture using a performance prediction neural network having multiple performance prediction parameters, the performance prediction neural network being configured to process the data specifying the candidate architecture in accordance with current values of the performance prediction parameters to generate a performance prediction that characterizes how well a neural network having the candidate architecture would perform after being trained on the particular machine learning task; and generating an updated set of candidate architectures by selecting one or more of the candidate architectures in the current set based on the performance predictions for the candidate architectures in the current set.

Neural architecture search using a performance prediction neural network

As visual recognition scales up to ever larger numbers of categories, maintaining high accuracy is increasingly difficult. Embodiment of the present invention include methods for optimizing accuracy-specificity trade-offs in large scale recognition where object categories form a semantic hierarchy consisting of many levels of abstraction.

Method and system for optimizing accuracy-specificity trade-offs in large scale visual recognition

A method is provided for classifying an image. The method includes inferring location information of an object of interest in an input representation of the image. The method further includes determining foreground object features and background object features from the input representation of the image. The method additionally includes pooling the foreground object features separately from the background object features using the location information to form a new representation of the image. The new representation is different than the input representation of the image. The method also includes classifying the image based on the new representation of the image.

Object-centric spatial pooling for image classification

Growing community of inventors

Stanford, CA, United States of America

Fei-Fei Li