公开(公告)号：US20250021880A1

公开(公告)日：2025-01-16

申请号：US18767837

申请日：2024-07-09

Applicant: Nokia Solutions and Networks Oy

Inventor： Dan Kushnir ,  Shiyun Xu

IPC: G06N20/00

Abstract: In some embodiments, there is provided receiving, as an input to a first machine learning model, a plurality of data; learning, by the first machine learning model and based at least on the plurality of data, a latent space; generating, based on the plurality of data and the latent space, a proximity graph, wherein label knowledge from labeled data is diffused on a plurality of nodes of the proximity graph; filtering, by the proximity graph, the plurality of nodes to provide a top k most uncertain nodes, wherein the top k most uncertain nodes form a subset of a plurality of unlabeled data; and providing the subset of the plurality of unlabeled data to a second machine learning model comprised in an active learning process. Related system, methods, and articles of manufacture are also disclosed.

公开(公告)号：US20230376793A1

公开(公告)日：2023-11-23

申请号：US17749427

申请日：2022-05-20

Applicant: Nokia Solutions and Networks Oy

Inventor： Gerald Lehmann ,  Dan Kushnir ,  Maria Able ,  Gerald Meyer ,  Huseyin Uzunalioglu ,  Robert Seidl

IPC: G06N5/02

CPC classification number: G06N5/022

Abstract: Systems, methods, and software for training a machine learning model. The system utilizes training data to train the machine learning model across multiple epochs. The system prepares additional training data by: selecting a set of samples that are unclassified, operating the machine learning model to predict labels that classify the samples, determining an uncertainty of the labels predicted by the machine learning model, calculating a ranking score for each of the samples in the set, selecting a subset of the samples that have more than a threshold ranking score, and submitting the subset to a client for replacement labels. The system receives the replacement labels from the client, and trains the machine learning model, using the subset of the samples as the training data. The labels predicted by the machine learning model for the subset are replaced with corresponding replacement labels from the client.