Qualcomm Incorporated

Alibaba Group Holding Limited

Chao Cheng

Ning Zhang

Kai Shi

Yuan Xie

Minghai Qin

Xiaoxin Fan

Meriam Khufu Ragheb Rezk

Srinivas Jasti

Methods and systems are provided for implementing training of learning models, including obtaining a pre-trained weight set for a learning model on a sample dataset and on a first loss function; selecting at least two tasks having heterogeneous features to be computed by a reference model; obtaining a reference dataset for the at least two tasks; designating a second loss function for feature embedding between the heterogeneous features of the at least two tasks; training the learning model on the first loss function and training the reference model on the second loss function, in turn; and updating the weight set based on a feature embedding learned by the learning model and a feature embedding learned by the reference model, in turn. Methods and systems of the present disclosure may alleviate computational overhead incurred by executing the learning model and loading different weight sets at a central network of the model.

Multi-task segmented learning models

A method and apparatus are disclosed for searching for a radar signal within signals received by a wireless device. The wireless device may receive signals within a first frequency segment and a second frequency segment, which is adjacent to the first frequency segment. The wireless device may determine Fast Fourier Transform (FFT) bins associated with the first frequency segment and the second frequency segment. The wireless device may combine the FFT bins associated with the first frequency segment and the second frequency segment and may search for the radar signal within the combined FFT bins.

Radar detection for adjacent segments in wireless communications

Growing community of inventors

Santa Clara, CA, United States of America

Chao Cheng