公开(公告)号：US20250094819A1

公开(公告)日：2025-03-20

申请号：US18471184

申请日：2023-09-20

Applicant: NVIDIA CORPORATION

Inventor： Wonmin BYEON ,  Sudarshan BABU ,  Shalini DE MELLO ,  Jan KAUTZ

IPC: G06N3/096 ,  G06N3/0455

Abstract: One embodiment of the present invention sets forth a technique for executing a transformer neural network. The technique includes executing a first attention unit included in the transformer neural network to convert a first input token into a first query, a first key, and a first plurality of values, where each value included in the first plurality of values represents a sub-task associated with the transformer neural network. The technique also includes computing a first plurality of outputs associated with the first input token based on the first query, the first key, and the first plurality of values. The technique further includes performing a task associated with an input corresponding to the first input token based on the first input token and the first plurality of outputs.

公开(公告)号：US20250103906A1

公开(公告)日：2025-03-27

申请号：US18471196

申请日：2023-09-20

Applicant: NVIDIA CORPORATION

Inventor： Wonmin BYEON ,  Sudarshan BABU ,  Shalini DE MELLO ,  Jan KAUTZ

IPC: G06N3/0985 ,  G06N3/0895

Abstract: One embodiment of the present invention sets forth a technique for performing meta-learning. The technique includes performing a first set of training iterations to convert a prediction learning network into a first trained prediction learning network based on a first support set of training data and executing a representation learning network and the first trained prediction learning network to generate a first set of supervised training output and a first set of self-supervised training output based on a first query set of training data corresponding to the first support set of training data. The technique also includes performing a first training iteration to convert the representation learning network into a first trained representation learning network based on a first loss associated with the first set of supervised training output and a second loss associated with the first set of self-supervised training output.

公开(公告)号：US20250095350A1

公开(公告)日：2025-03-20

申请号：US18471209

申请日：2023-09-20

Applicant: NVIDIA CORPORATION

Inventor： Wonmin BYEON ,  Sudarshan BABU ,  Shalini DE MELLO ,  Jan KAUTZ

IPC: G06V10/82 ,  G06V10/776

Abstract: One embodiment of the present invention sets forth a technique for executing a machine learning model. The technique includes performing a first set of training iterations to convert a prediction learning network into a first trained prediction learning network based on a first support set associated with a first set of classes. The technique also includes executing a first trained representation learning network to convert a first data sample into a first latent representation, where the first trained representation learning network is generated by training a representation learning network using a first query set, a first set of self-supervised losses, and a first set of supervised losses. The technique further includes executing the first trained prediction learning network to convert the first latent representation into a first prediction of a first class that is not included in the second set of classes.

公开(公告)号：US20250094813A1

公开(公告)日：2025-03-20

申请号：US18471204

申请日：2023-09-20

Applicant: NVIDIA CORPORATION

Inventor： Wonmin BYEON ,  Sudarshan BABU ,  Shalini DE MELLO ,  Jan KAUTZ

IPC: G06N3/0895

Abstract: One embodiment of the present invention sets forth a technique for training a transformer neural network. The technique includes inputting a first task token and a first set of samples into the transformer neural network and training the transformer neural network using a first set of losses between predictions generated by the transformer neural network from the first task token and first set of samples as well as a first set of labels. The technique also includes converting the first task token into a second task token that is larger than the first task token, inputting the second task token and a second set of samples into the transformer neural network, and training the transformer neural network using a second set of losses between predictions generated by the transformer neural network from the second task token and the second set of samples as well as a second set of labels.