25.8k citations · 74.2k across the 10 of their papers we have counts for
11 papers
Pathways: Asynchronous Distributed Dataflow for ML
Paul Barham, Aakanksha Chowdhery, Jeff Dean +13
We present the design of a new large scale orchestration layer for accelerators. Our system, Pathways, is explicitly designed to enable exploration of new systems and ML research i…
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design
Jeffrey Dean
The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abiliti…
The Case for Learned Index Structures
Tim Kraska, Alex Beutel, Ed H. Chi +2
Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a re…
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Yonghui Wu, Mike Schuster, Zhifeng Chen +28
Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based tr…
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi, Ashish Agarwal, Paul Barham +37
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed…
Distilling the Knowledge in a Neural Network
Geoffrey Hinton, Oriol Vinyals, Jeff Dean
A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfo…