HIGH-CAPACITY MACHINE LEARNING SYSTEM

摘要

The present disclosure is directed to a high-capacity training and prediction machine learning platform that can support high-capacity parameter models (e.g., with 10 billion weights). The platform implements a generic feature transformation layer for joint updating and a distributed training framework utilizing shard servers to increase training speed for the high-capacity model size. The models generated by the platform can be utilized in conjunction with existing dense baseline models to predict compatibilities between different groupings of objects (e.g., a group of two objects, three objects, etc.).

主权项

1. A method, comprising:
applying at least two learning models to multiple data sets indicative of features associated with tuples of objects, each tuple being a pairing of a first object and a second object; updating parameters of a prediction model based on a linear combination of at least two result sets corresponding to the at least two learning models; receiving a request for identifying a compatible object for a given object based on respective features of the compatible object and the given object; responsive to the received request, determining, based on the updated parameters of the prediction model, a prediction value associated with the given object, the prediction value indicative of a probability of compatibility based on the respective features; and identifying the compatible object for the given object based on the prediction value.