T
Tao Wang
Researcher at Peking University
Publications - 406
Citations - 15638
Tao Wang is an academic researcher from Peking University. The author has contributed to research in topics: Photoluminescence & Quantum well. The author has an hindex of 43, co-authored 374 publications receiving 12684 citations. Previous affiliations of Tao Wang include Nanjing University & Google.
Papers
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Reading Digits in Natural Images with Unsupervised Feature Learning
TL;DR: A new benchmark dataset for research use is introduced containing over 600,000 labeled digits cropped from Street View images, and variants of two recently proposed unsupervised feature learning methods are employed, finding that they are convincingly superior on benchmarks.
Proceedings Article
End-to-end text recognition with convolutional neural networks
TL;DR: This paper combines the representational power of large, multilayer neural networks together with recent developments in unsupervised feature learning, which allows them to use a common framework to train highly-accurate text detector and character recognizer modules.
Proceedings Article
Deep learning with COTS HPC systems
TL;DR: This paper presents technical details and results from their own system based on Commodity Off-The-Shelf High Performance Computing (COTS HPC) technology: a cluster of GPU servers with Infiniband interconnects and MPI, and shows that it can scale to networks with over 11 billion parameters using just 16 machines.
Posted Content
An Empirical Evaluation of Deep Learning on Highway Driving
Brody Huval,Tao Wang,Sameep Tandon,Jeff Kiske,Will Song,Joel Pazhayampallil,Mykhaylo Andriluka,Pranav Rajpurkar,Toki Migimatsu,Royce Cheng-Yue,Fernando A. Mujica,Adam Coates,Andrew Y. Ng +12 more
TL;DR: It is shown how existing convolutional neural networks can be used to perform lane and vehicle detection while running at frame rates required for a real-time system, lending credence to the hypothesis that deep learning holds promise for autonomous driving.
Proceedings Article
Convolutional neural networks over tree structures for programming language processing
TL;DR: In this article, a tree-based convolutional neural network (TBCNN) is proposed for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information.