Prompt learning has been proven to be highly effective in improving
pre-...
It is increasingly important to enable privacy-preserving inference for ...
Self-supervised learning (SSL) is a commonly used approach to learning a...
Fully homomorphic encryption (FHE) is a powerful encryption technique th...
In Natural Language Processing (NLP), intelligent neuron models can be
s...
Fully homomorphic encryption (FHE) protects data privacy in cloud comput...
Emerging self-supervised learning (SSL) has become a popular image
repre...
Performing neural network inference on encrypted data without decryption...
Vision Transformers (ViTs) have demonstrated the state-of-the-art perfor...
Factorizing a large matrix into small matrices is a popular strategy for...
Fully Homomorphic Encryption over the Torus (TFHE) allows arbitrary
comp...
Pre-trained language models such as BERT have shown remarkable effective...
Recently Homomorphic Encryption (HE) is used to implement Privacy-Preser...
How to improve the efficiency of routing procedures in CapsNets has been...
Billions of text analysis requests containing private emails, personal t...
Nanopore genome sequencing is the key to enabling personalized medicine,...
Hybrid Privacy-Preserving Neural Network (HPPNN) implementing linear lay...
Big data is one of the cornerstones to enabling and training deep neural...
Homomorphic Encryption (HE) is one of the most promising security soluti...
In this paper, we propose a hierarchical deep reinforcement learning
(DR...