Training machine learning models using unsupervised data augmentation
Номер патента: EP3942479A1
Опубликовано: 26-01-2022
Автор(ы): Qizhe Xie, Quoc V. Le, Thang Minh Luong, Zihang Dai
Принадлежит: Google LLC
Опубликовано: 26-01-2022
Автор(ы): Qizhe Xie, Quoc V. Le, Thang Minh Luong, Zihang Dai
Принадлежит: Google LLC
Реферат: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes receiving training data comprising a plurality of unlabeled training inputs and a plurality of labeled training inputs; generating augmented training data, comprising generating, for each of the plurality of unlabeled training inputs, a respective augmented training input by applying a data augmentation technique to the unlabeled training input; and training the machine learning model on the augmented training data. In particular, but not exclusively, the model may be trained for perceptual tasks (e.g. tasks relating to vision or speech).
Curated sentiment analysis in multi-layer, machine learning-based forecasting model using customized, commodity-specific neural networks
Номер патента: WO2022026881A1. Автор: Thomas Blair,Tony Lei,Spyros LAZARIS,Alex KURZHANSKIY,Leo JOLICOEUR,Michael MCERLEAN,Craig Forman. Владелец: Agblox, Inc.. Дата публикации: 2022-02-03.