公开(公告)号：US20240362460A1

公开(公告)日：2024-10-31

申请号：US18626833

申请日：2024-04-04

申请人： Google LLC

发明人： Li Zhang ,  Yandong Li ,  Yin Cui ,  Hong-You Chen ,  Mingda Zhang

IPC分类号： G06N3/0455 ,  G06N3/084

CPC分类号： G06N3/0455 ,  G06N3/084

摘要： The technology relates to providing personalized neural network-based models according to user input, which can be generated upon request or otherwise as needed. This may include receiving, by one or more processors of a computing device, input corresponding to a task description. Then the input corresponding to the task description is encoded into a set of text embeddings. Based on this, the system applies mixer prediction to the set of text embeddings to generate a set of mixers and learns a set of basis models according to the set of mixers. The set of basis models are combined to form a single personalized model corresponding to the task description. This personalized model can then be used in video understanding, quality assessment, providing a recommendation, performing a classification, or performing a search.

公开(公告)号：US20240119307A1

公开(公告)日：2024-04-11

申请号：US18474934

申请日：2023-09-26

申请人： Google LLC

发明人： Hong-You Chen ,  Boqing Gong ,  Mingda Zhang ,  Hang Qi ,  Xuhui Jia ,  Li Zhang

IPC分类号： G06N3/098

CPC分类号： G06N3/098

摘要： The embodiments are directed towards providing personalized federated learning (PFL) models via sharable federated basis models. A model architecture and learning algorithm for PFL models is disclosed. The embodiments learn a set of basis models, which can be combined layer by layer to form a personalized model for each client using specifically learned combination coefficients. The set of basis models are shared with each client of a set of the clients. Thus, the set of basis models is common to each client of the set of clients. However, each client may generate a unique PFL based on their specifically learned combination coefficients. The unique combination of coefficients for each client may be encoded in a separate personalized vector for each of the clients.

公开(公告)号：US20230297852A1

公开(公告)日：2023-09-21

申请号：US18007379

申请日：2021-07-29

申请人： Google LLC

发明人： Li Zhang ,  Andrew Gerald Howard ,  Brendan Wesley Jou ,  Yukun Zhu ,  Mingda Zhang ,  Andrey Zhmoginov

IPC分类号： G06N5/022

CPC分类号： G06N5/022

摘要： Example implementations of the present disclosure combine efficient model design and dynamic inference. With a standalone lightweight model, the unnecessary computation on easy examples is avoided and the information extracted by the lightweight model also guide the synthesis of a specialist network from the basis models. With extensive experiments on ImageNet it is shown that a proposed example BasisNet is particularly effective for image classification and a BasisNet-MV3 achieves 80.3% top-1 accuracy with 290 M MAdds without early termination.