Mar 4, 2021 � We propose a new training protocol, Bootstrap Aggregation of Teacher Ensembles (BATE), which is applicable to various types of machine learning models.

Mar 1, 2021 � With the leverage of a differential privacy algorithm in a high-performance computing environment, we propose a new training protocol, Bootstrap�...

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance.

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance.

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance.

With the leverage of a differential privacy algorithm in a high-performance computing environment, we present the Bootstrap Aggregation of Teacher Ensembles�...

There is a need to transfer knowledge among institutions and organizations to save effort in annotation and labeling or in enhancing task performance.

Bibliographic details on Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles.

Jul 10, 2023 � The Private Aggregation of Teacher Ensembles (PATE) scheme is one promising approach to address this privacy concern while supporting knowledge�...

Privacy-Preserving Knowledge Transfer with Bootstrap Aggregation of Teacher Ensembles � Privacy Preserving 100% � Knowledge Transfer 100% � Machine Learning 66%.