公开(公告)号：US20250005327A1

公开(公告)日：2025-01-02

申请号：US18691766

申请日：2022-09-08

Applicant: Solventum Intellectual Properties Company

Inventor： Yang Liu ,  Hua Cheng ,  Russell I. Klopfer ,  Thomas Schaaf ,  Matthew R. Gormley

IPC: G06N3/045

Abstract: Systems and techniques are described for configuring and training a neural network including receiving a plurality of documents, providing the received documents to a neural network model comprising a deep convolutional-based encoder including a plurality of squeeze-and-excitation (SE) and residual convolutional modules that form a plurality of SE/residual convolutional block pairs, determining a word embedding matrix for the plurality of documents, providing one or more word embeddings in the word embedding matrix to the encoder, generating one or more label-specific representations based on the output of the plurality of SE/residual convolutional block pairs, computing a probability of a label being present in the one or more documents given the one or more label specific representations and using a first loss function to train the model for frequently occurring labels and a second loss function to train the model for rarely occurring labels.