Google Inc.

Arris Enterprises LLC

Carnegie Mellon University

Heng-Tze Cheng

Tushar Deepak Chandra

Tal Shaked

Jeremiah Harmsen

Hrishikesh Balkrishna Aradhye

Gregory Sean Corrado

Paul C Davis

Wei Chai

Glen Anderson

Jianguo Li

Di You

Mustafa Ispir

Alexandre Tachard Passos

Rohan Anil

Mehmet Levent Koc

Xiaobing Liu

Shahar Jamshy

David Edgar Lluncor

A system includes one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the computers to implement a combined machine learning model for processing an input including multiple features to generate a predicted output for the machine learning input. The combined model includes: a deep machine learning model configured to process the features to generate a deep model output; a wide machine learning model configured to process the features to generate a wide model output; and a combining layer configured to process the deep model output generated by the deep machine learning model and the wide model output generated by the wide machine learning model to generate the predicted output, in which the deep model and the wide model have been trained jointly on training data to generate the deep model output and the wide model output.

Wide and deep machine learning models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.

Factorized models

A method for automatic recognition of human activity is provided and includes the steps of decomposing human activity into a plurality of fundamental component attributes needed to perform an activity and defining ontologies of fundamental component attributes from the plurality of the fundamental component attributes identified during the decomposing step for each of a plurality of different targeted activities. The method also includes the steps of converting a data stream captured during a performance of an activity performed by a human into a sequence of fundamental component attributes and classifying the performed activity as one of the plurality of different targeted activities based on a closest match of the sequence of fundamental component attributes obtained during the converting step to at least a part of one of the ontologies of fundamental component attributes defined during the defining step. A system for performing the method is also disclosed.

System and method for activity recognition

Growing community of inventors

Sunnyvale, CA, United States of America

Heng-Tze Cheng