公开(公告)号：US20250037006A1

公开(公告)日：2025-01-30

申请号：US18225970

申请日：2023-07-25

Applicant: ADOBE INC.

Inventor： Kanak MAHADIK ,  Sungchul KIM ,  Ryan ROSSI ,  Handong ZHAO ,  Shravika MITTAL

IPC: G06N20/00

Abstract: In various examples, a ranking is generated for a set of computing instances based on predicted metrics associated with computing instances. For example, a prediction model estimates various system performance metrics based on information associated with a workload and configuration information associated with computing instances. The system performance metrics estimated by the prediction model are used to rank the set of computing instances.

公开(公告)号：US20240273296A1

公开(公告)日：2024-08-15

申请号：US18625884

申请日：2024-04-03

Applicant: Adobe Inc.

Inventor： Sungchul KIM ,  Subrata MITRA ,  Ruiyi Zhang ,  Rui Wang ,  Handong ZHAO ,  Tong YU

IPC: G06F40/295 ,  G06N20/00

CPC classification number: G06F40/295 ,  G06N20/00

Abstract: Embodiments of the technology described herein describe a machine classifier capable of continually learning new classes through a continual few-shot learning approach. A natural language processing (NLP) machine classifier may initially be trained to identify a plurality of other classes through a conventional training process. In order to learn a new class, natural-language training data for a new class is generated. The training data for the new class may be few-shot training data. The training also uses synthetic training data that represents each of the plurality of other classes. The synthetic training data may be generated through a model inversion of the original classifier. The synthetic training data and the natural-language training data are used to retrain the NLP classifier to identify text in the plurality of other classes and the new class using.