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Found 5 papers, 2 papers with code
Secure Quantized Training for Deep Learning
We implement training of neural networks in secure multi-party computation (MPC) using quantization commonly used in said setting.
Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers
Thus, data augmentation methods have been developed to improve classifiers by artificially created training data.
Effectiveness of MPC-friendly Softmax Replacement
Softmax is widely used in deep learning to map some representation to a probability distribution.
Secure Evaluation of Quantized Neural Networks
Prior works provide protocols that only work on fixed-point integers and specialized activation functions, two aspects that are not supported by popular Machine Learning frameworks, and the need for these specialized model representations means that it is hard, and often impossible, to use e. g., TensorFlow to design, train and test models that later have to be evaluated securely.
A Note on Our Submission to Track 4 of iDASH 2019
iDASH is a competition soliciting implementations of cryptographic schemes of interest in the context of biology.
