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公开(公告)号:US20240202529A1
公开(公告)日:2024-06-20
申请号:US18068987
申请日:2022-12-20
Applicant: QUALCOMM Incorporated
Inventor: Joseph Binamira SORIAGA , Hossein HOSSEINI
IPC: G06N3/084
CPC classification number: G06N3/084
Abstract: Certain aspects of the present disclosure provide techniques for improved machine learning. A data tensor is generated as output from a layer of a neural network. A first subset of the first data tensor and a second subset of the first data tensor are generated using a tensor splitting operation. The second subset of the first data tensor is stored, and the first subset of the first data tensor is provided to a subsequent layer of the neural network. One or more parameters of the layer of the neural network are refined based at least in part on the stored second subset of the first data tensor.
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公开(公告)号:US20230222335A1
公开(公告)日:2023-07-13
申请号:US17997400
申请日:2021-06-11
Applicant: QUALCOMM Incorporated
Inventor: Hossein HOSSEINI , Christos LOUIZOS , Joseph Binamira SORIAGA
Abstract: Certain aspects of the present disclosure provide techniques for authenticating a user based on a machine learning model, including receiving user authentication data associated with a user; generating output from a neural network model based on the user authentication data; determining a distance between the output and an embedding vector associated with the user; comparing the determined distance to a distance threshold; and making an authentication decision based on the comparison.
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公开(公告)号:US20230058415A1
公开(公告)日:2023-02-23
申请号:US17409725
申请日:2021-08-23
Applicant: QUALCOMM Incorporated
Inventor: Susu XU , Tijmen Pieter Frederik BLANKEVOORT , Arash BEHBOODI , Hossein HOSSEINI
Abstract: A method for generating an artificial neural network (ANN) model includes initializing weights of a first neural network model. The weight of the first neural network model are updated using adversarial training to approximate a function for predicting an output of a second neural network model.
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公开(公告)号:US20220383197A1
公开(公告)日:2022-12-01
申请号:US17828613
申请日:2022-05-31
Applicant: QUALCOMM Incorporated
Inventor: Hyunsin PARK , Hossein HOSSEINI , Sungrack YUN , Kyu Woong HWANG
Abstract: Certain aspects of the present disclosure provide techniques for training a machine learning model. The method generally includes receiving, at a local device from a server, information defining a global version of a machine learning model. A local version of the machine learning model and a local center associated with the local version of the machine learning model are generated based on embeddings generated from local data at a client device and the global version of the machine learning model. A secure center different from the local center is generated based, at least in part, on information about secure centers shared by a plurality of other devices participating in a federated learning scheme. Information about the local version of the machine learning model and information about the secure center is transmitted by the local device to the server.
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公开(公告)号:US20230169350A1
公开(公告)日:2023-06-01
申请号:US18040111
申请日:2021-09-28
Applicant: QUALCOMM Incorporated
Inventor: Christos LOUIZOS , Hossein HOSSEINI , Matthias REISSER , Max WELLING , Joseph Binamira SORIAGA
IPC: G06N3/098
CPC classification number: G06N3/098
Abstract: Aspects described herein provide techniques for performing federated learning of a machine learning model, comprising: for each respective client of a plurality of clients and for each training round in a plurality of training rounds: generating a subset of model elements for the respective client based on sampling a gate probability distribution for each model element of a set of model elements for a global machine learning model; transmitting to the respective client: the subset of model elements; and a set of gate probabilities based on the sampling, wherein each gate probability of the set of gate probabilities is associated with one model element of the subset of model elements; receiving from each respective client of the plurality of clients a respective set of model updates; and updating the global machine learning model based on the respective set of model updates from each respective client of the plurality of clients.
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公开(公告)号:US20220318412A1
公开(公告)日:2022-10-06
申请号:US17223946
申请日:2021-04-06
Applicant: QUALCOMM Incorporated
Inventor: Yunhui GUO , Hossein HOSSEINI , Christos LOUIZOS , Joseph Binamira SORIAGA
Abstract: Certain aspects of the present disclosure provide techniques for improved machine learning using private variational dropout. A set of parameters of a global machine learning model is updated based on a local data set, and the set of parameters is pruned based on pruning criteria. A noise-augmented set of gradients is computed for a subset of parameters remaining after the pruning, based in part on a noise value, and the noise-augmented set of gradients is transmitted to a global model server.
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公开(公告)号:US20220108194A1
公开(公告)日:2022-04-07
申请号:US17491094
申请日:2021-09-30
Applicant: QUALCOMM Incorporated
Inventor: Mohammad SAMRAGH RAZLIGHI , Hossein HOSSEINI , Kambiz AZARIAN YAZDI , Joseph Binamira SORIAGA
IPC: G06N5/04
Abstract: Certain aspects of the present disclosure provide techniques for inferencing with a split inference model, including: generating an initial feature vector based on a client-side split inference model component; generating a modified feature vector by modifying a null-space component of the initial feature vector; providing the modified feature vector to a server-side split inference model component on a remote server; and receiving an inference from the remote server.
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