公开(公告)号：US20240161460A1

公开(公告)日：2024-05-16

申请号：US18501167

申请日：2023-11-03

Applicant: Qualcomm Technologies, Inc.

Inventor： Pengwan YANG ,  Yuki Markus ASANO ,  Cornelis Gerardus Maria SNOEK

IPC: G06V10/77 ,  G06V10/42 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70

CPC classification number: G06V10/7715 ,  G06V10/42 ,  G06V10/764 ,  G06V10/774 ,  G06V10/82 ,  G06V20/70

Abstract: Certain aspects of the present disclosure provide techniques and apparatuses for inferencing against a multidimensional point cloud using a machine learning model. An example method generally includes generating a score for each respective point in a multidimensional point cloud using a scoring neural network. Points in the multidimensional point cloud are ranked based on the generated score for each respective point in the multidimensional point cloud. The top points are selected from the ranked multidimensional point cloud, and one or more actions are taken based on the selected top k points.

公开(公告)号：US20250103882A1

公开(公告)日：2025-03-27

申请号：US18583291

申请日：2024-02-21

Applicant: QUALCOMM Technologies, Inc.

Inventor： Dawid Jan KOPICZKO ,  Yuki Markus ASANO ,  Tijmen Pieter Frederik BLANKEVOORT

IPC: G06N3/08

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for efficiently adapting a machine learning model from a base task to a downstream task based on frozen matrices. An example method generally includes receiving an input for processing through a layer of a neural network. An output of the layer of the neural network is generated based on a first product, the first product being based on a first trainable scaling vector, a first frozen matrix, a second trainable scaling vector, a second frozen matrix, and the received input.

公开(公告)号：US20250005336A1

公开(公告)日：2025-01-02

申请号：US18710150

申请日：2023-01-24

Applicant: QUALCOMM TECHNOLOGIES, INC.

Inventor： Phillip LIPPE ,  Yuki Markus ASANO ,  Sara MAGLIACANE ,  Taco Sebastiaan COHEN ,  Efstratios GAVVES

IPC: G06N3/0475 ,  G06V10/82

Abstract: A processor-implemented method for causal representation learning of temporal effects includes receiving, via an artificial neural network (ANN), temporal sequence data for high-dimensional observations. The ANN generates a latent representation based on latent variables for the temporal sequence data. The latent variables of the temporal sequence data are assigned to causal variables. The ANN determines a representation of causal factors for each dimension of the temporal sequence databased on the assignment.