公开(公告)号：US20240176994A1

公开(公告)日：2024-05-30

申请号：US18551844

申请日：2021-07-26

Applicant: QUALCOMM TECHNOLOGIES, INC.

Inventor： Phillip LIPPE ,  Taco Sebastiaan COHEN ,  Efstratios GAVVES

IPC: G06N3/0464 ,  G06N3/09

CPC classification number: G06N3/0464 ,  G06N3/09

Abstract: A method for generating a causal graph includes receiving a data set including observation data and intervention data corresponding to multiple variables. A probability distribution is determined for each variable based on the observation data. A likelihood of including each edge in the graph is computed based on the probability distribution and the intervention data. Each edge is a causal connection between variables of the multiple variables. The graph is generated based on the likelihood of including each edge. The graph may be updated by iteratively repeating the determination of the probability distribution and the computing of the likelihood of including each edge.

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.

公开(公告)号：US20230308666A1

公开(公告)日：2023-09-28

申请号：US17681675

申请日：2022-02-25

Applicant: QUALCOMM Technologies, Inc.

Inventor： Frank BRONGERS ,  Phillip LIPPE ,  Sara MAGLIACANE

IPC: H04N19/20 ,  H04N19/436 ,  G06V20/40 ,  G06V10/776 ,  G06V10/82

CPC classification number: H04N19/20 ,  G06V10/776 ,  G06V10/82 ,  G06V20/41 ,  H04N19/436

Abstract: A computer-implemented method for contrastive object representation from temporal data using an artificial neural network (ANN) includes receiving, by the ANN, a video. The video comprises a temporal sequence of frames including images of one or more objects. The ANN generates object representations corresponding to the one or more objects based on temporal data of multiple frames of the temporal sequence of frames. The object representations are communicated to a receiver.