Linnan Wang

According to our database, Linnan Wang authored at least 26 papers between 2015 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2022

Building an Intelligent Agent to Design Neural Networks.

Linnan Wang

PhD thesis, 2022

Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search.

Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

IEEE Trans. Pattern Anal. Mach. Intell., 2022

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs.

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit-Florea

CoRR, 2022

Multi-objective Optimization by Learning Space Partition.

Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian

Proceedings of the Tenth International Conference on Learning Representations, 2022

Searching the Deployable Convolution Neural Networks for GPUs.

Linnan Wang, Chenhan Yu, Satish Salian, Slawomir Kierat, Szymon Migacz, Alex Fit-Florea

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Multi-objective Optimization by Learning Space Partitions.

Yiyang Zhao, Linnan Wang, Kevin Yang, Tianjun Zhang, Tian Guo, Yuandong Tian

CoRR, 2021

Learning Space Partitions for Path Planning.

Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Few-Shot Neural Architecture Search.

Yiyang Zhao, Linnan Wang, Yuandong Tian, Rodrigo Fonseca, Tian Guo

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search.

Linnan Wang, Rodrigo Fonseca, Yuandong Tian

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks.

Linnan Wang, Wei Wu, Junyu Zhang, Hang Liu, George Bosilca, Maurice Herlihy, Rodrigo Fonseca

Proceedings of the HPDC '20: The 29th International Symposium on High-Performance Parallel and Distributed Computing, 2020

Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Sample-Efficient Neural Architecture Search by Learning Action Space.

Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, Yuandong Tian

CoRR, 2019

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai, Yuandong Tian, Rodrigo Fonseca

CoRR, 2019

2018

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks.

Linnan Wang, Wei Wu, Yiyang Zhao, Junyu Zhang, Hang Liu, George Bosilca, Jack J. Dongarra, Maurice Herlihy, Rodrigo Fonseca

CoRR, 2018

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search.

Linnan Wang, Yiyang Zhao, Yuu Jinnai

CoRR, 2018

Superneurons: dynamic GPU memory management for training deep neural networks.

Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska

Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2018

Warp-Consolidation: A Novel Execution Model for GPUs.

Ang Li, Weifeng Liu, Linnan Wang, Kevin J. Barker, Shuaiwen Leon Song

Proceedings of the 32nd International Conference on Supercomputing, 2018

ADAPT: an event-based adaptive collective communication framework.

Xi Luo, Wei Wu, George Bosilca, Thananon Patinyasakdikul, Linnan Wang, Jack J. Dongarra

Proceedings of the 27th International Symposium on High-Performance Parallel and Distributed Computing, 2018

Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition.

Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018

2017

Accelerating deep neural network training with inconsistent stochastic gradient descent.

Linnan Wang, Yi Yang, Renqiang Min, Srimat T. Chakradhar

Neural Networks, 2017

Efficient Communications in Training Large Scale Neural Networks.

Yiyang Zhao, Linnan Wang, Wei Wu, George Bosilca, Richard W. Vuduc, Jinmian Ye, Wenqi Tang, Zenglin Xu

Proceedings of the on Thematic Workshops of ACM Multimedia 2017, Mountain View, CA, USA, October 23, 2017

Simple and efficient parallelization for probabilistic temporal tensor factorization.

Guangxi Li, Zenglin Xu, Linnan Wang, Jinmian Ye, Irwin King, Michael R. Lyu

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Towards Interactive Data Exploration.

Carsten Binnig, Fuat Basik, Benedetto Buratti, Ugur Çetintemel, Yeounoh Chung, Andrew Crotty, Cyrus Cousins, Dylan Ebert, Philipp Eichmann, Alex Galakatos, Benjamin Hättasch, Amir Ilkhechi, Tim Kraska, Zeyuan Shang, Isabella Tromba, Arif Usta, Prasetya Ajie Utama, Eli Upfal, Linnan Wang, Nathaniel Weir, Robert C. Zeleznik, Emanuel Zgraggen

Proceedings of the Real-Time Business Intelligence and Analytics, 2017

2016

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing.

Linnan Wang, Wei Wu, Zenglin Xu, Jianxiong Xiao, Yi Yang

Proceedings of the 2016 International Conference on Supercomputing, 2016

2015

Large Scale Artificial Neural Network Training Using Multi-GPUs.

Linnan Wang, Wei Wu, Jianxiong Xiao, Yang Yi

CoRR, 2015

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing.

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

CoRR, 2015