公开(公告)号：US20230137905A1

公开(公告)日：2023-05-04

申请号：US18089513

申请日：2022-12-27

Applicant: Intel Corporation

Inventor： Amrutha Machireddy ,  Ranganath Krishnan ,  Nilesh Ahuja ,  Omesh Tickoo

IPC: G06N3/091

Abstract: Disclosed is an example solution to perform source-free active adaptation to distributional shifts for machine learning. The example solution includes: interface circuitry; programmable circuitry; and instructions to cause the programmable circuitry to: perform a first training of a neural network on a baseline data set associated with a first data distribution; compare data of a shifted data set to a threshold uncertainty value, wherein the threshold uncertainty value is associated with a distributional shift between the baseline data set and the shifted data set; generate a shifted data subset including items of the shifted dataset that satisfy the threshold uncertainty value; and perform a second training of the neural network based on the shifted data subset.

公开(公告)号：US20220319162A1

公开(公告)日：2022-10-06

申请号：US17845732

申请日：2022-06-21

Applicant: Intel Corporation

Inventor： Srivatsa Rangachar Srinivasa ,  Tanay Karnik ,  Dileep Kurian ,  Ranganath Krishnan ,  Jainaveen Sundaram Priya ,  Indranil Chakraborty

IPC: G06V10/84 ,  G06F7/544 ,  G06V10/82

Abstract: Methods, apparatus, systems, and articles of manufacture providing a Bayesian compute unit with reconfigurable sampler and methods and apparatus to operate the same are disclosed. An example apparatus includes a number generator to generate a sequence of numbers; a multiplier to generate a plurality of products by multiplying respective numbers of the sequence of the numbers by a variance value; and an adder to generate a plurality of weights by adding a mean value to the plurality of products, the plurality of weights corresponding to a single probability distribution.

公开(公告)号：US20210110264A1

公开(公告)日：2021-04-15

申请号：US17129789

申请日：2020-12-21

Applicant: Intel Corporation

Inventor： Leobardo E. Campos Macias ,  Ranganath Krishnan ,  David Gomez Gutierrez ,  Rafael De La Guardia Gonzalez ,  Nilesh Ahuja ,  Javier Felip Leon ,  Jose I. Parra Vilchis ,  Anthony K. Guzman Leguel

IPC: G06N3/08 ,  G06N3/04

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate knowledge sharing among neural networks. An example apparatus includes a trainer to train, at a first computing system, a first Bayesian Neural Network (BNN) on a first subset of training data to generate a first weight distribution, and train, at a second computing system, a second BNN on a second subset of the training data to generate a second weight distribution, the second subset of the training data different from the first subset of training data. The example apparatus includes a knowledge sharing controller to generate a third BNN based on the first weight distribution and the second weight distribution.

公开(公告)号：US20200326667A1

公开(公告)日：2020-10-15

申请号：US16911100

申请日：2020-06-24

Applicant: Intel Corporation

Inventor： Nilesh Ahuja ,  Ignacio J. Alvarez ,  Ranganath Krishnan ,  Ibrahima J. Ndiour ,  Mahesh Subedar ,  Omesh Tickoo

IPC: G05B13/02 ,  G06N3/08 ,  G06N7/00 ,  G06N5/04 ,  G06K9/62

Abstract: Techniques are disclosed for using neural network architectures to estimate predictive uncertainty measures, which quantify how much trust should be placed in the deep neural network (DNN) results. The techniques include measuring reliable uncertainty scores for a neural network, which are widely used in perception and decision-making tasks in automated driving. The uncertainty measurements are made with respect to both model uncertainty and data uncertainty, and may implement Bayesian neural networks or other types of neural networks.

公开(公告)号：US11586854B2

公开(公告)日：2023-02-21

申请号：US16830341

申请日：2020-03-26

Applicant: Intel Corporation

Inventor： Nilesh Ahuja ,  Ibrahima Ndiour ,  Javier Felip Leon ,  David Gomez Gutierrez ,  Ranganath Krishnan ,  Mahesh Subedar ,  Omesh Tickoo

IPC: G06K9/00 ,  G06K9/62 ,  G05B13/04 ,  G06N3/084 ,  G05D1/00 ,  G05D1/02 ,  B60W60/00 ,  G05B13/02 ,  G06V20/20 ,  G06V20/58 ,  G06V40/10

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.

公开(公告)号：US10803676B2

公开(公告)日：2020-10-13

申请号：US16284965

申请日：2019-02-25

Applicant: Intel Corporation

Inventor： Ignacio J. Alvarez ,  Ranganath Krishnan

IPC: G06T19/00 ,  G06T19/20 ,  G06T17/00 ,  G06T17/20 ,  H04L29/06

Abstract: According to various embodiments, devices, methods, and computer-readable media for reconstructing a 3D scene are described. A server device, sensor devices, and client devices may interoperate to reconstruct a 3D scene sensed by the sensor devices. The server device may generate one or more models for objects in the scene, including the identification of dynamic and/or static objects. The sensor devices may, provide model data updates based on these generated models, such that only delta changes in the scene may be provided, in addition to raw sensor data. Models may utilize semantic knowledge, such as knowledge of the venue or identity of one or more persons in the scene, to further facilitate model generation and updating. Other embodiments may be described and/or claimed.

公开(公告)号：US20190259221A1

公开(公告)日：2019-08-22

申请号：US16284965

申请日：2019-02-25

Applicant: Intel Corporation

Inventor： Ignacio J. Alvarez ,  Ranganath Krishnan

IPC: G06T19/20 ,  H04L29/06 ,  G06T17/00 ,  G06T17/20

Abstract: According to various embodiments, devices, methods, and computer-readable media for reconstructing a 3D scene are described. A server device, sensor devices, and client devices may interoperate to reconstruct a 3D scene sensed by the sensor devices. The server device may generate one or more models for objects in the scene, including the identification of dynamic and/or static objects. The sensor devices may, provide model data updates based on these generated models, such that only delta changes in the scene may be provided, in addition to raw sensor data. Models may utilize semantic knowledge, such as knowledge of the venue or identity of one or more persons in the scene, to further facilitate model generation and updating. Other embodiments may be described and/or claimed.

公开(公告)号：US11889396B2

公开(公告)日：2024-01-30

申请号：US17720351

申请日：2022-04-14

Applicant: Intel Corporation

Inventor： Richard Dorrance ,  Ignacio Alvarez ,  Deepak Dasalukunte ,  S M Iftekharul Alam ,  Sridhar Sharma ,  Kathiravetpillai Sivanesan ,  David Israel Gonzalez Aguirre ,  Ranganath Krishnan ,  Satish Jha

IPC: H04W4/46 ,  G08G1/01 ,  H04W72/04

CPC classification number: H04W4/46 ,  G08G1/0141 ,  H04W72/04

Abstract: A communication device for a vehicle to communicate features about the vehicle's environment includes one or more processors configured to receive a communication from another device, wherein the communication includes a global reference coordinate system for an area covered by the other device and a number of allowed transmissions to be sent from the vehicle; transform stored data about the vehicle's environment based on the global reference coordinate system; divide the transformed stored data into a plurality of subsets of data; and select one or more subsets of data from the plurality of subsets for transmission according to the number of allowed transmissions.

公开(公告)号：US20210309264A1

公开(公告)日：2021-10-07

申请号：US17134331

申请日：2020-12-26

Applicant: Intel Corporation

Inventor： Javier Felip Leon ,  Nilesh Ahuja ,  Leobardo Campos Macias ,  Rafael De La Guardia Gonzalez ,  David Gomez Gutierrez ,  David Israel Gonzalez Aguirre ,  Anthony Kyung Guzman Leguel ,  Ranganath Krishnan ,  Jose Ignacio Parra Vilchis

IPC: B60W60/00 ,  G05B13/04 ,  G05D1/02 ,  B25J9/16

Abstract: A human-robot collaboration system, including at least one processor; and a non-transitory computer-readable storage medium including instructions that, when executed by the at least one processor, cause the at least one processor to: predict a human atomic action based on a probability density function of possible human atomic actions for performing a predefined task; and plan a motion of the robot based on the predicted human atomic action.

公开(公告)号：US20200226430A1

公开(公告)日：2020-07-16

申请号：US16830341

申请日：2020-03-26

Applicant: Intel Corporation

Inventor： Nilesh Ahuja ,  Ibrahima Ndiour ,  Javier Felip Leon ,  David Gomez Gutierrez ,  Ranganath Krishnan ,  Mahesh Subedar ,  Omesh Tickoo

IPC: G06K9/62 ,  G06K9/00 ,  G05B13/04 ,  G05B13/02 ,  G06N3/08 ,  G05D1/00 ,  G05D1/02 ,  B60W60/00

Abstract: Vehicle navigation control systems in autonomous driving rely on accurate predictions of objects within the vicinity of the vehicle to appropriately control the vehicle safely through its surrounding environment. Accordingly this disclosure provides methods and devices which implement mechanisms for obtaining contextual variables of the vehicle's environment for use in determining the accuracy of predictions of objects within the vehicle's environment.