公开(公告)号：US20220383117A1

公开(公告)日：2022-12-01

申请号：US17824832

申请日：2022-05-25

Applicant: QUALCOMM Incorporated

Inventor： Jangho KIM ,  Juntae LEE ,  Simyung CHANG

IPC: G06N3/08 ,  G06N5/04

Abstract: A computer-implemented method for generating s personalized neural network model includes accessing a shared or global neural network model. One or more personal inputs of a user are received. A set of features of the one or more inputs is extracted. An approximation of a posterior probability is computed based on the extracted features. A set of personalized weights are generated based on the approximated posterior probability. Processing one or more subsequent inputs via a personal model, including the set of personalized weights, enables generating of an inference.

公开(公告)号：US20220318633A1

公开(公告)日：2022-10-06

申请号：US17705248

申请日：2022-03-25

Applicant: QUALCOMM Incorporated

Inventor： Jangho KIM ,  Simyung CHANG ,  Hyunsin PARK ,  Juntae LEE ,  Jaewon CHOI ,  Kyu Woong HWANG

IPC: G06N3/08 ,  G06N3/04

Abstract: A processor-implemented method for compressing a deep neural network model includes receiving an initial neural network model. The initial neural network is pruned based on a first threshold to generate a pruned network and a set of pruned weights. A quantization process is applied to the pruned network to produce a pruned and quantized network. A teacher model is generated by incorporating the pruned set of weights with the pruned network. In addition, an initial student model is generated from the quantized and pruned network. The initial student model is trained using the teacher model to output a trained student model.

公开(公告)号：US20220121949A1

公开(公告)日：2022-04-21

申请号：US17506646

申请日：2021-10-20

Applicant: QUALCOMM Incorporated

Inventor： Simyung CHANG ,  Jangho KIM ,  Hyunsin PARK ,  Juntae LEE ,  Jaewon CHOI ,  Kyu Woong HWANG

IPC: G06N3/08 ,  G06K9/62

Abstract: A method for generating a personalized model includes receiving one or more personal data samples from a user. A prototype of a personal identity is generated based on the personal data samples. The prototype of the personal identity is trained to reflect personal characteristics of the user. A network graph is generated based on the prototype of the personal identity. One or more channels of a global network are pruned based on the network graph to produce the personalized model.