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    ADAPTABLE AND CONTINUALLY LEARNING NEURAL NETWORK ARCHITECTURE
    2.
    发明申请
    ADAPTABLE AND CONTINUALLY LEARNING NEURAL NETWORK ARCHITECTURE 有权

    公开(公告)号：US20250094810A1

    公开(公告)日：2025-03-20

    申请号：US18823282

    申请日：2024-09-03

    Applicant: SRI International

    Inventor： Zachary A. DANIELS ,  Jun HU ,  Michael R. LOMNITZ ,  Philip MILLER ,  Aswin NADAMUNI RAGHAVAN ,  Yuzheng ZHANG ,  Michael PIACENTINO ,  David C. ZHANG ,  Michael ISNARDI ,  Saurabh FARKYA

    IPC: G06N3/084

    Abstract: Method and apparatus for processing input information using an adaptable and continually learning neural network architecture comprising an encoder, at least one adaptor and at least one reconfigurator. The encoder, at least one reconfigurator and at least one adaptor determine whether the input information is out-of-distribution or in-distribution. If the input information is in distribution, the architecture extracts features from the input information, creates hyperdimensional vectors representing the features and classifies the hyperdimensional vectors. If the input information is out of distribution, the architecture creates at least one adaptor to operate with the encoder and the at least one reconfigurator to extract features from the input information, create hyperdimensional vectors representing the features and classify the hyperdimensional vectors.

    SYSTEM DESIGN FOR AN INTEGRATED LIFELONG MACHINE LEARNING AGENT
    3.
    发明公开
    SYSTEM DESIGN FOR AN INTEGRATED LIFELONG MACHINE LEARNING AGENT 审中-公开

    公开(公告)号：US20240202538A1

    公开(公告)日：2024-06-20

    申请号：US18535928

    申请日：2023-12-11

    Applicant: SRI International

    Inventor： Aswin NADAMUNI RAGHAVAN ,  Indranil SUR ,  Zachary DANIELS ,  Jesse HOSTETLER ,  Abrar RAHMAN ,  Ajay DIVAKARAN ,  Michael R. PIACENTINO

    IPC: G06N3/092

    CPC classification number: G06N3/092

    Abstract: A method, apparatus and system for lifelong reinforcement learning include receiving features of a task, communicating the task features to a learning system, where the learning system learns or performs a task related to the features based on learning or performing similar previous tasks, determining from the features if the task has changed and if so, communicating the features of the changed task to the learning system, where the learning system learns or performs the changed task based on learning or performing similar previous tasks, automatically annotating feature characteristics of received features including differences between the features of the original task and the features of the changed task to enable the learning system to more efficiently learn or perform at least the changed task, and if the task has not changed, processing the task features of a current task by the learning system to learn or perform the current task.

    OBJECT DETECTION AND VISUAL GROUNDING AT THE EDGE
    4.
    发明申请
    OBJECT DETECTION AND VISUAL GROUNDING AT THE EDGE 有权

    公开(公告)号：US20250069356A1

    公开(公告)日：2025-02-27

    申请号：US18815217

    申请日：2024-08-26

    Applicant: SRI International

    Inventor： Aswin NADAMUNI RAGHAVAN ,  Jun HU ,  David C. ZHANG ,  Michael R. LOMNITZ ,  Yuzheng ZHANG ,  Michael PIACENTINO ,  Philip MILLER ,  Zachary A. DANIELS ,  Saurabh FARKYA ,  Abrar A. RAHMAN ,  Abdelrahman SHARAFELDIN

    IPC: G06V10/25 ,  G06V10/74

    Abstract: A method, apparatus, and system for object detection on an edge device include projecting a hyperdimensional vector of a query request for an image received at the edge device into a hyperdimensional embedding space to identify at least one exemplar in the hyperdimensional embedding space having a predetermined measure of similarity to the query request using a network trained to: generate a respective hyperdimensional image vector and a respective hyperdimensional text vector for the image and received text descriptions of the image, generate a hyperdimensional query text vector of the query request, combine and embed respective ones of the hyperdimensional image vectors and the hyperdimensional text vectors into a hyperdimensional embedding space to generate respective exemplars, project the hyperdimensional query text vector into the hyperdimensional embedding space, and determine a similarity measure between the hyperdimensional query text vector and at least one of the respective exemplars.

    EDGE DEVICE HAVING A HETEROGENOUS NEUROMORPHIC COMPUTING ARCHITECTURE
    7.
    发明申请
    EDGE DEVICE HAVING A HETEROGENOUS NEUROMORPHIC COMPUTING ARCHITECTURE 有权

    公开(公告)号：US20220198782A1

    公开(公告)日：2022-06-23

    申请号：US17553239

    申请日：2021-12-16

    Applicant: SRI International

    Inventor： David Chao ZHANG ,  Michael R. PIACENTINO ,  Aswin NADAMUNI RAGHAVAN

    IPC: G06V10/774 ,  G06N3/04 ,  G06N3/08

    Abstract: An edge device comprising a feature extractor and a reconfigurator. The feature extractor comprises a first neural network for encoding input information into data vectors and extracting particular data vectors representing features within the input information, wherein the first neural network comprises at least one encoder layer and at least one adaptor layer. The reconfigurator is coupled to the feature extractor and comprises a second neural network for classifying the particular data vectors and wherein, upon requiring additional features to be extracted, the reconfigurator adapts at least one layer in the first neural network, second neural network or both by performing at least one of: (1) altering weights, (2) adding layers, (3) deleting layers, (4) reordering layers to improve classification of particular data vector. The first neural network, the second neural network or both are trained using gradient-free training.

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