• Publications
  • Influence
TensorFlow: A system for large-scale machine learning
TLDR
The TensorFlow dataflow model is described and the compelling performance that Tensor Flow achieves for several real-world applications is demonstrated.
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TLDR
The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
Deep Learning with Differential Privacy
TLDR
This work develops new algorithmic techniques for learning and a refined analysis of privacy costs within the framework of differential privacy, and demonstrates that deep neural networks can be trained with non-convex objectives, under a modest privacy budget, and at a manageable cost in software complexity, training efficiency, and model quality.
Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data
TLDR
Private Aggregation of Teacher Ensembles (PATE) is demonstrated, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users, which achieves state-of-the-art privacy/utility trade-offs on MNIST and SVHN.
Adversarial Patch
TLDR
A method to create universal, robust, targeted adversarial image patches in the real world, which can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class.
TensorFlow: learning functions at scale
TLDR
This talk describes Tensor Flow and outlines some of its applications, and discusses the question of what TensorFlow and deep learning may have to do with functional programming.
Learning to Protect Communications with Adversarial Neural Cryptography
TLDR
It is demonstrated that the neural networks can learn how to perform forms of encryption and decryption, and also how to apply these operations selectively in order to meet confidentiality goals.
Learning a Natural Language Interface with Neural Programmer
TLDR
This paper presents the first weakly supervised, end-to-end neural network model to induce such programs on a real-world dataset, and enhances the objective function of Neural Programmer, a neural network with built-in discrete operations, and applies it on WikiTableQuestions, a natural language question-answering dataset.
Dynamic control flow in large-scale machine learning
TLDR
This paper describes the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system, and describes the use of dataflow graphs to represent machine learning models, offering several distinctive features.
A computational model for TensorFlow: an introduction
TLDR
The paper describes an operational semantics, of the kind common in the literature on programming languages, that suggests that a programming-language perspective is fruitful in designing and in explaining systems such as TensorFlow.
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