J
Jianmin Chen
Researcher at Google
Publications - 4
Citations - 17184
Jianmin Chen is an academic researcher from Google. The author has contributed to research in topics: Multi-core processor & Dataflow. The author has an hindex of 3, co-authored 3 publications receiving 14201 citations.
Papers
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Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
Posted Content
TensorFlow: A system for large-scale machine learning
Martín Abadi,Paul Barham,Jianmin Chen,Zhifeng Chen,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Geoffrey Irving,Michael Isard,Manjunath Kudlur,Josh Levenberg,Rajat Monga,Sherry Moore,Derek G. Murray,Benoit Steiner,Paul A. Tucker,Vijay K. Vasudevan,Pete Warden,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +21 more
TL;DR: The TensorFlow dataflow model is described and the compelling performance that Tensor Flow achieves for several real-world applications is demonstrated.
Posted Content
Revisiting Distributed Synchronous SGD
TL;DR: It is demonstrated that a third approach, synchronous optimization with backup workers, can avoid asynchronous noise while mitigating for the worst stragglers and is empirically validated and shown to converge faster and to better test accuracies.
Journal ArticleDOI
Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data
TL;DR: In this paper , a tree-guided feature selection and logic aggregation approach was proposed for large-scale regression with rare binary features, in which dimension reduction was achieved through not only a sparsity pursuit but also an aggregation promoter with the logic operator of “or.