Sri International

Anirban Roy

Ajay Divakaran

Girish Acharya

John James Byrnes

Dayne B Freitag

Karan Sikka

Yi Yao

Michael Wessel

Susmit Jha

Karuna Ahuja

Louise Yarnall

Zachary Weiler

In general, the disclosure describes techniques for characterizing a dynamical system and a neural ordinary differential equation (NODE)-based controller for the dynamical system. An example analysis system is configured to: obtain a set of parameters of a NODE model used to implement the NODE-based controller, the NODE model trained to control the dynamical system; determine, based on the set of parameters, a system property of a combined system comprising the dynamical system and the NODE-based controller, the system property comprising one or more of an accuracy, safety, reliability, reachability, or controllability of the combined system; and output the system property to modify one or more of the dynamical system or the NODE-based controller to meet a required specification for the combined system.

Analysis and design of dynamical system controllers using neural differential equations

This disclosure describes machine learning techniques for capturing human knowledge for performing a task. In one example, a video device obtains video data of a first user performing the task and one or more sensors generate sensor data during performance of the task. An audio device obtains audio data describing performance of the task. A computation engine applies a machine learning system to correlate the video data to the audio data and sensor data to identify portions of the video, sensor, and audio data that depict a same step of a plurality of steps for performing the task. The machine learning system further processes the correlated data to update a domain model defining performance of the task. A training unit applies the domain model to generate training information for performing the task. An output device outputs the training information for use in training a second user to perform the task.

Machine learning system for technical knowledge capture

A method, apparatus and system for understanding visual content includes determining at least one region proposal for an image, attending at least one symbol of the proposed image region, attending a portion of the proposed image region using information regarding the attended symbol, extracting appearance features of the attended portion of the proposed image region, fusing the appearance features of the attended image region and features of the attended symbol, projecting the fused features into a semantic embedding space having been trained using fused attended appearance features and attended symbol features of images having known descriptive messages, computing a similarity measure between the projected, fused features and fused attended appearance features and attended symbol features embedded in the semantic embedding space having at least one associated descriptive message and predicting a descriptive message for an image associated with the projected, fused features.

Aligning symbols and objects using co-attention for understanding visual content

Growing community of inventors

San Francisco, CA, United States of America

Anirban Roy