Google Inc.

Michael Sahngwon Ryoo

Anelia Angelova

Anthony Jacob Piergiovanni

Alexander Toshev

Mingxing Tan

Mostafa Dehghani

Anurag Arnab

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing a network input using a computer vision neural network with learned tokenization.

Computer vision neural networks with learned tokenization

Generally, the present disclosure is directed to a neural architecture search process for finding small and fast video processing networks for understanding of video data. The neural architecture search process can automatically design networks that provide comparable video processing performance at a fraction of the computational and storage cost of larger existing models, thereby conserving computing resources such as memory and processor usage.

Small and fast video processing networks via neural architecture search

Techniques are disclosed that enable the generation of predicted sequences of terminals using a generator model portion of a prediction model. Various implementations include controlling actuators of a robot based on the predicted sequences of terminals. Additional or alternative implementations include jointly training the generator model portion of the prediction model using a discriminator model portion of the prediction model using, for example, stochastic adversarial based sampling.

Future prediction, using stochastic adversarial based sampling, for robotic control and/or other purpose(s)

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining one or more neural network architectures of a neural network for performing a video processing neural network task. In one aspect, a method comprises: at each of a plurality of iterations: selecting a parent neural network architecture from a set of neural network architectures; training a neural network having the parent neural network architecture to perform the video processing neural network task, comprising determining trained values of connection weight parameters of the parent neural network architecture; generating a new neural network architecture based at least in part on the trained values of the connection weight parameters of the parent neural network architecture; and adding the new neural network architecture to the set of neural network architectures.

Growing community of inventors

Mountain View, CA, United States of America

Michael Sahngwon Ryoo