王林楠 (Linnan Wang)

Office 351, CIT
Department of Computer Science
Brown University
Providence, RI 02906

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My Mission:

Artificial Intelligence (AI) is going to be the extension of our brains, in the same way as cars are the extension of our legs. It has already been an indispensable part of our life. Every day, AI navigates us to places, answers our queries, and recommends restaurants and movies. Overall, it amplifies what we do, augmenting our memory, giving you instant knowledge, allowing us to concentrate on doing things that are properly human.

Designing new AI models is still reserved for experts; and the goal of my research is to democratize AI, making it accessible to everybody, such that any person regardless of their prior experiences, and any company regardless of size can deploy sophisticated AI solutions with only a few simple clicks.

I'm a Ph.D. student at the CS department of Brown University, advised by Prof.Rodrigo Fonseca. Before Brown, I was a OMSCS student at Gatech while being a full time software developer at Dow Jones. I acquired my bachelor degree from University of Electronic Science and Technology of China (UESTC) at the beautiful Qing Shui He campus in 2011. In addition to my awesome advisor, I also work closely with Yiyang Zhao, Yuandong Tian, Saining Xie, Yi Yang, Wei Wu, and George Bosilca.

in Submission:

Wang, Linnan, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian
Sample-Efficient Neural Architecture Search by Learning Action Space
Paper 

Wang, Linnan, Wei Wu, Yiyang Zhao, Hang Liu, George Bosilca, Jack Dongarra, Maurice Herlihy, Rodrigo Fonseca
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks
Paper 

Publications:

2020

Wang, Linnan, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca
Neural Architecture Search using Deep Neural Networks and Monte Carlo Tree Search
To appear in the Thirty-Fourth AAAI conference on Artificial Intelligence (AAAI-2020)
Paper ·  Code  

2018

Luo, Xi , Wei Wu, George Bosilca, Thananon Patinyasakdikul, Linnan Wang, Jack Dongarra
ADAPT: An Event-based Adaptive Collective Communication Framework
In Proceedings of 27th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC2018)
Paper 

Li, Ang, Weifeng Liu, Linnan Wang, Kevin Barker, Shuaiwen Leon Song
Warp-Consolidation: A Novel Execution Model for Modern GPUs
In Proceedings of the 2018 International Conference on Supercomputing (ICS2018)
Paper 

Ye, Jinmian, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
In Proceedings of 31th IEEE Conference on Computer Vision and Pattern Recognition (CVPR2018)
Paper ·  Poster 

Wang, Linnan, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska
SuperNeurons:Dynamic GPU Memory Management for Training Deep Nonlinear Neural Networks
In Proceedings of the 23nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP2018)
Paper ·  Talk ·  Presentation  ·  Code

2017

Wang, Linnan, Yi Yang, Renqiang Min, and Srimat Chakradhar
Accelerating Deep Neural Network Training with Inconsistent Stochastic Gradient Descent
Neural Networks (2017)
Paper ·  Patent

Zhao, Yiyang, Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Jinmian Ye, Wenqi Tang, and Zenglin Xu.
Efficient Communications in Training Large Scale Neural Networks
In Proceedings of the 25th ACM international conference on Multimedia (MM2017)
Paper

Li, Guangxi, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, and Michael Lyu
Simple and Efficient Parallelization for Probabilistic Temporal Tensor Factorization
In 2017 International Joint Conference on Neural Networks (IJCNN2017)
Paper

2016

Wang, Linnan, Wei Wu, Zenglin Xu, Jianxiong Xiao, and Yi Yang
BLASX: A High Performance Level-3 BLAS Library for Heterogeneous MultiGPU Computing
In Proceedings of the 2016 International Conference on Supercomputing (ICS2016)
Paper ·  SC Poster ·  Code ·  Presentation

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