We present a framework for experimenting with secure multi-party computation directly in TensorFlow.
In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting.
#98 best model for Image Classification on ImageNet
We detail a new framework for privacy preserving deep learning and discuss its assets.
In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered.
Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline.
We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data.
SOTA for Fake News Detection on Grover-Mega