Search Results for author:
Found 8 papers, 6 papers with code
Dynamic Control Flow in Large-Scale Machine Learning
We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system.
Adversarial Patch
We present a method to create universal, robust, targeted adversarial image patches in the real world.
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
The recent, remarkable growth of machine learning has led to intense interest in the privacy of the data on which machine learning relies, and to new techniques for preserving privacy.
Learning to Protect Communications with Adversarial Neural Cryptography
We ask whether neural networks can learn to use secret keys to protect information from other neural networks.
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users.
Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.
TensorFlow: A system for large-scale machine learning
TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
