Sri International

Sri Inlernational

Mohamed R Amer

Timothy J Shields

Aswin Nadamuni Raghavan

Timothy J Meo

Sek Meng Chai

Amir Tamrakar

David Chao Zhang

Xiao Lin

Alex C Tozzo

Bhaskar S Ramamurthy

Ajay Divakaran

Karen L Myers

Saad Masood Khan

Chih-Hung Yeh

Behjat Siddiquie

Colleen Richey

David A Salter

Dejan Jovanovic

Timur Almaev

Hapreet S Sawhney

Max Ehrlich

Kyung-Yoon Kim

Technologies for analyzing multi-task multimodal data to detect multi-task multimodal events using a deep multi-task representation learning, are disclosed. A combined model with both generative and discriminative aspects is used to share information during both generative and discriminative processes. The technologies can be used to classify data and also to generate data from classification events. The data can then be used to morph data into a desired classification event.

Deep multi-task representation learning

Artificial neural network systems involve the receipt by a computing device of input data that defines a pattern to be recognized (such as faces, handwriting, and voices). The computing device may then decompose the input data into a first subband and a second subband, wherein the first and second subbands include different characterizing features of the pattern in the input data. The first and second subbands may then be fed into first and second neural networks being trained to recognize the pattern. Reductions in power expenditure, memory usage, and time taken, for example, allow resource-limited computing devices to perform functions they otherwise could not.

Low precision neural networks using subband decomposition

Techniques are disclosed for using a computation engine executing a machine learning system to generate, according to constraints, renderings of a building or building information modeling (BIM) data for the building, wherein the constraints include at least one of an architectural style or a building constraint. In one example, an input device is configured to receive an input indicating one or more surfaces for the building and one or more constraints. A machine learning system executed by a computation engine is configured to apply a model, trained using images of buildings labeled with corresponding constraints for the buildings, to the one or more surfaces for the building to generate at least one of a rendering of the one or more surfaces for the building according to the constraints or BIM data for the building according to the constraints. Further, the machine learning system is configured to output the rendering or the BIM data.

Machine learning system for building renderings and building information modeling data

This disclosure describes techniques that include generating, based on a description of a scene, a movie or animation that represents at least one possible version of a story corresponding to the description of the scene. This disclosure also describes techniques for training a machine learning model to generate predefined data structures from textual information, visual information, and/or other information about a story, an event, a scene, or a sequence of events or scenes within a story. This disclosure also describes techniques for using GANs to generate, from input, an animation of motion (e.g., an animation or a video clip). This disclosure also describes techniques for implementing an explainable artificial intelligence system that may provide end users with information (e.g., through a user interface) that enables an understanding of at least some of the decisions made by the AI system.

Explainable artificial intelligence

Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

Systems and methods for optimizing operations of computing devices using deep neural networks

Applying artificial intelligence to generate motion information

Artificial intelligence for generating structured descriptions of scenes

Artificial intelligence in interactive storytelling

Technologies for analyzing temporal components of multimodal data to detect short-term multimodal events, determine relationships between short-term multimodal events, and recognize long-term multimodal events, using a deep learning architecture, are disclosed.

Dynamic hybrid models for multimodal analysis

Growing community of inventors

Brooklyn, NY, United States of America

Mohamed R Amer