Mingxing Tan
Mingxing Tan
Google Brain
Verified email at google.com
Cited by
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Efficientnet: Rethinking model scaling for convolutional neural networks
M Tan, QV Le
arXiv preprint arXiv:1905.11946, 2019
Mnasnet: Platform-aware neural architecture search for mobile
M Tan, B Chen, R Pang, V Vasudevan, M Sandler, A Howard, QV Le
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
Searching for mobilenetv3
A Howard, M Sandler, G Chu, LC Chen, B Chen, M Tan, W Wang, Y Zhu, ...
Proceedings of the IEEE International Conference on Computer Vision, 1314-1324, 2019
Elasticflow: A complexity-effective approach for pipelining irregular loop nests
M Tan, G Liu, R Zhao, S Dai, Z Zhang
2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 78-85, 2015
Architectural specialization for inter-iteration loop dependence patterns
S Srinath, B Ilbeyi, M Tan, G Liu, Z Zhang, C Batten
2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 583-595, 2014
Multithreaded pipeline synthesis for data-parallel kernels
M Tan, B Liu, S Dai, Z Zhang
2014 IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 718-725, 2014
Flushing-enabled loop pipelining for high-level synthesis
S Dai, M Tan, K Hao, Z Zhang
2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC), 1-6, 2014
MixConv: Mixed Depthwise Convolutional Kernels
M Tan, QV Le
Proceedings of the 30th British Machine Vision Conference (BMVC), 2019
Efficientdet: Scalable and efficient object detection
M Tan, R Pang, QV Le
arXiv preprint arXiv:1911.09070, 2019
Area-efficient pipelining for FPGA-targeted high-level synthesis
R Zhao, M Tan, S Dai, Z Zhang
Proceedings of the 52nd Annual Design Automation Conference, 1-6, 2015
Casa: Correlation-aware speculative adders
G Liu, Y Tao, M Tan, Z Zhang
2014 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2014
Adversarial Examples Improve Image Recognition
C Xie, M Tan, B Gong, J Wang, A Yuille, QV Le
arXiv preprint arXiv:1911.09665, 2019
Mapping-aware constrained scheduling for LUT-based FPGAs
M Tan, S Dai, U Gupta, Z Zhang
Proceedings of the 2015 ACM/SIGDA International Symposium on Field …, 2015
Evolutionary-Neural Hybrid Agents for Architecture Search
K Maziarz, A Khorlin, Q de Laroussilhe, S Jastrzębski, M Tan, ...
arXiv preprint arXiv:1811.09828, 2018
Assemblenet: Searching for multi-stream neural connectivity in video architectures
MS Ryoo, AJ Piergiovanni, M Tan, A Angelova
arXiv preprint arXiv:1905.13209, 2019
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
X Du, TY Lin, P Jin, G Ghiasi, M Tan, Y Cui, QV Le, X Song
arXiv preprint arXiv:1912.05027, 2019
Bignas: Scaling up neural architecture search with big single-stage models
J Yu, P Jin, H Liu, G Bender, PJ Kindermans, M Tan, T Huang, X Song, ...
arXiv preprint arXiv:2003.11142, 2020
Search to Distill: Pearls are Everywhere but not the Eyes
Y Liu, X Jia, M Tan, R Vemulapalli, Y Zhu, B Green, X Wang
arXiv preprint arXiv:1911.09074, 2019
Neural Architecture Search with Factorized Hierarchical Search Space
M Tan, Q Le, B Chen, V Vasudevan, R Pang
US Patent App. 16/258,927, 2020
When Ensembling Smaller Models is More Efficient than Single Large Models
D Kondratyuk, M Tan, M Brown, B Gong
arXiv preprint arXiv:2005.00570, 2020
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