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Mingxing Tan
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2020 – today
- 2024
- [c45]Zhaoqi Leng, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection. ICRA 2024: 4238-4244 - [c44]Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, James Guo, Yin Zhou, Mingxing Tan, Weilong Yang, Congcong Li:
STT: Stateful Tracking with Transformers for Autonomous Driving. ICRA 2024: 4442-4449 - [c43]Kan Chen, Runzhou Ge, Hang Qiu, Rami Ai-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov:
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting. ICRA 2024: 4766-4773 - [i40]Longlong Jing, Ruichi Yu, Xu Chen, Zhengli Zhao, Shiwei Sheng, Colin Graber, Qi Chen, Qinru Li, Shangxuan Wu, Han Deng, Sangjin Lee, Chris Sweeney, Qiurui He, Wei-Chih Hung, Tong He, Xingyi Zhou, Farshid Moussavi, Zijian Guo, Yin Zhou, Mingxing Tan, Weilong Yang, Congcong Li:
STT: Stateful Tracking with Transformers for Autonomous Driving. CoRR abs/2405.00236 (2024) - [i39]Zhaoqi Leng, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
PVTransformer: Point-to-Voxel Transformer for Scalable 3D Object Detection. CoRR abs/2405.02811 (2024) - [i38]Jyh-Jing Hwang, Runsheng Xu, Hubert Lin, Wei-Chih Hung, Jingwei Ji, Kristy Choi, Di Huang, Tong He, Paul Covington, Benjamin Sapp, James Guo, Dragomir Anguelov, Mingxing Tan:
EMMA: End-to-End Multimodal Model for Autonomous Driving. CoRR abs/2410.23262 (2024) - 2023
- [j3]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Kenji Kawaguchi, Hanxiao Liu, Adams Wei Yu, Jiahui Yu, Yi-Ting Chen, Minh-Thang Luong, Yonghui Wu, Mingxing Tan, Quoc V. Le:
Combined scaling for zero-shot transfer learning. Neurocomputing 555: 126658 (2023) - [c42]Sheng Li, Garrett Andersen, Tao Chen, Liqun Cheng, Julian Grady, Da Huang, Quoc V. Le, Andrew Li, Xin Li, Yang Li, Chen Liang, Yifeng Lu, Yun Ni
, Ruoming Pang, Mingxing Tan, Martin Wicke, Gang Wu, Shengqi Zhu, Parthasarathy Ranganathan, Norman P. Jouppi:
Hyperscale Hardware Optimized Neural Architecture Search. ASPLOS (3) 2023: 343-358 - [c41]Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations. ICRA 2023: 7039-7045 - [c40]Tong He, Pei Sun, Zhaoqi Leng, Chenxi Liu, Dragomir Anguelov, Mingxing Tan:
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection. IROS 2023: 1637-1644 - [i37]Kan Chen, Runzhou Ge, Hang Qiu, Rami Ai-Rfou, Charles R. Qi, Xuanyu Zhou, Zoey Yang, Scott Ettinger, Pei Sun, Zhaoqi Leng, Mustafa Baniodeh, Ivan Bogun, Weiyue Wang, Mingxing Tan, Dragomir Anguelov:
WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting. CoRR abs/2304.03834 (2023) - [i36]Tong He, Pei Sun, Zhaoqi Leng, Chenxi Liu, Dragomir Anguelov, Mingxing Tan:
LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection. CoRR abs/2309.16870 (2023) - 2022
- [j2]Reza Mahjourian
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving. IEEE Robotics Autom. Lett. 7(2): 5639-5646 (2022) - [c39]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CVPR 2022: 17161-17170 - [c38]Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov:
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds. ECCV (21) 2022: 158-175 - [c37]Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Dragomir Anguelov:
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds. ECCV (10) 2022: 426-442 - [c36]Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov:
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds. ECCV (31) 2022: 555-572 - [c35]Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Jay Shi, Shuyang Cheng, Dragomir Anguelov:
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. ICLR 2022 - [c34]Yanqi Zhou, Xuanyi Dong, Tianjian Meng, Mingxing Tan, Berkin Akin, Daiyi Peng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Towards the Co-design of Neural Networks and Accelerators. MLSys 2022 - [i35]Reza Mahjourian, Jinkyu Kim, Yuning Chai, Mingxing Tan, Benjamin Sapp, Dragomir Anguelov:
Occupancy Flow Fields for Motion Forecasting in Autonomous Driving. CoRR abs/2203.03875 (2022) - [i34]Yingwei Li, Adams Wei Yu, Tianjian Meng, Benjamin Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Bo Wu, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan L. Yuille, Mingxing Tan:
DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection. CoRR abs/2203.08195 (2022) - [i33]Tianjian Meng, Golnaz Ghiasi, Reza Mahjourian, Quoc V. Le, Mingxing Tan:
Revisiting Multi-Scale Feature Fusion for Semantic Segmentation. CoRR abs/2203.12683 (2022) - [i32]Zhaoqi Leng, Mingxing Tan, Chenxi Liu, Ekin Dogus Cubuk, Xiaojie Shi, Shuyang Cheng, Dragomir Anguelov:
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions. CoRR abs/2204.12511 (2022) - [i31]Chenxi Liu, Zhaoqi Leng, Pei Sun, Shuyang Cheng, Charles R. Qi, Yin Zhou, Mingxing Tan, Dragomir Anguelov:
LidarNAS: Unifying and Searching Neural Architectures for 3D Point Clouds. CoRR abs/2210.05018 (2022) - [i30]Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu, Fei Xia, Zhaoqi Leng, Dragomir Anguelov:
SWFormer: Sparse Window Transformer for 3D Object Detection in Point Clouds. CoRR abs/2210.07372 (2022) - [i29]Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang, Xiao Zhang, Jonathon Shlens, Mingxing Tan, Dragomir Anguelov:
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds. CoRR abs/2210.13428 (2022) - [i28]Zhaoqi Leng, Guowang Li, Chenxi Liu, Ekin Dogus Cubuk, Pei Sun, Tong He, Dragomir Anguelov, Mingxing Tan:
LidarAugment: Searching for Scalable 3D LiDAR Data Augmentations. CoRR abs/2210.13488 (2022) - 2021
- [c33]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-based Algorithm for Approximating Self-Attention. AAAI 2021: 14138-14148 - [c32]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CVPR 2021: 3825-3834 - [c31]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CVPR 2021: 8085-8095 - [c30]Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong:
MoViNets: Mobile Video Networks for Efficient Video Recognition. CVPR 2021: 16020-16030 - [c29]Xiangning Chen, Cihang Xie
Robust and Accurate Object Detection via Adversarial Learning. CVPR 2021: 16622-16631 - [c28]Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan L. Yuille, Cihang Xie:
Shape-Texture Debiased Neural Network Training. ICLR 2021 - [c27]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. ICML 2021: 10096-10106 - [c26]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. IPDPS Workshops 2021: 947-950 - [c25]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. NeurIPS 2021: 3965-3977 - [i27]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Hanxiao Liu, Gabriel Bender, Adam Kraft, Chen Liang, Quoc V. Le:
PyGlove: Symbolic Programming for Automated Machine Learning. CoRR abs/2101.08809 (2021) - [i26]Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh:
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention. CoRR abs/2102.03902 (2021) - [i25]Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc V. Le, Norman P. Jouppi:
Searching for Fast Model Families on Datacenter Accelerators. CoRR abs/2102.05610 (2021) - [i24]Yanqi Zhou, Xuanyi Dong, Berkin Akin, Mingxing Tan, Daiyi Peng, Tianjian Meng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon:
Rethinking Co-design of Neural Architectures and Hardware Accelerators. CoRR abs/2102.08619 (2021) - [i23]Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong:
MoViNets: Mobile Video Networks for Efficient Video Recognition. CoRR abs/2103.11511 (2021) - [i22]Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong:
Robust and Accurate Object Detection via Adversarial Learning. CoRR abs/2103.13886 (2021) - [i21]Mingxing Tan, Quoc V. Le:
EfficientNetV2: Smaller Models and Faster Training. CoRR abs/2104.00298 (2021) - [i20]Zihang Dai, Hanxiao Liu, Quoc V. Le, Mingxing Tan:
CoAtNet: Marrying Convolution and Attention for All Data Sizes. CoRR abs/2106.04803 (2021) - [i19]Hieu Pham, Zihang Dai, Golnaz Ghiasi, Hanxiao Liu, Adams Wei Yu, Minh-Thang Luong, Mingxing Tan, Quoc V. Le:
Combined Scaling for Zero-shot Transfer Learning. CoRR abs/2111.10050 (2021) - 2020
- [c24]Cihang Xie
Adversarial Examples Improve Image Recognition. CVPR 2020: 816-825 - [c23]Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang:
Search to Distill: Pearls Are Everywhere but Not the Eyes. CVPR 2020: 7536-7545 - [c22]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CVPR 2020: 10778-10787 - [c21]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CVPR 2020: 11589-11598 - [c20]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. ECCV (23) 2020: 572-586 - [c19]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc Le:
BigNAS: Scaling up Neural Architecture Search with Big Single-Stage Models. ECCV (7) 2020: 702-717 - [c18]Michael S. Ryoo, A. J. Piergiovanni, Mingxing Tan, Anelia Angelova:
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures. ICLR 2020 - [c17]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. ICML 2020: 11546-11555 - [c16]Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, Quoc Le:
PyGlove: Symbolic Programming for Automated Machine Learning. NeurIPS 2020 - [i18]Jiahui Yu, Pengchong Jin, Hanxiao Liu, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Thomas S. Huang, Xiaodan Song, Ruoming Pang, Quoc V. Le:
BigNAS: Scaling Up Neural Architecture Search with Big Single-Stage Models. CoRR abs/2003.11142 (2020) - [i17]Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen:
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators. CoRR abs/2004.14525 (2020) - [i16]Dan Kondratyuk, Mingxing Tan, Matthew Brown, Boqing Gong:
When Ensembling Smaller Models is More Efficient than Single Large Models. CoRR abs/2005.00570 (2020) - [i15]Xuanyi Dong, Mingxing Tan, Adams Wei Yu, Daiyi Peng, Bogdan Gabrys
AutoHAS: Differentiable Hyper-parameter and Architecture Search. CoRR abs/2006.03656 (2020) - [i14]Cihang Xie, Mingxing Tan, Boqing Gong, Alan L. Yuille, Quoc V. Le:
Smooth Adversarial Training. CoRR abs/2006.14536 (2020) - [i13]Denny Zhou, Mao Ye, Chen Chen, Tianjian Meng, Mingxing Tan, Xiaodan Song, Quoc V. Le, Qiang Liu, Dale Schuurmans:
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks. CoRR abs/2007.00811 (2020) - [i12]Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan L. Yuille, Cihang Xie:
Shape-Texture Debiased Neural Network Training. CoRR abs/2010.05981 (2020) - [i11]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc V. Le, Xiaodan Song:
Efficient Scale-Permuted Backbone with Learned Resource Distribution. CoRR abs/2010.11426 (2020) - [i10]Arissa Wongpanich, Hieu Pham, James Demmel, Mingxing Tan, Quoc V. Le, Yang You, Sameer Kumar:
Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour. CoRR abs/2011.00071 (2020)
2010 – 2019
- 2019
- [c15]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. BMVC 2019: 74 - [c14]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CVPR 2019: 2820-2828 - [c13]Andrew Howard, Ruoming Pang, Hartwig Adam, Quoc V. Le, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu:
Searching for MobileNetV3. ICCV 2019: 1314-1324 - [c12]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. ICML 2019: 6105-6114 - [i9]Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam:
Searching for MobileNetV3. CoRR abs/1905.02244 (2019) - [i8]Mingxing Tan, Quoc V. Le:
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. CoRR abs/1905.11946 (2019) - [i7]Michael S. Ryoo, A. J. Piergiovanni, Mingxing Tan, Anelia Angelova:
AssembleNet: Searching for Multi-Stream Neural Connectivity in Video Architectures. CoRR abs/1905.13209 (2019) - [i6]Mingxing Tan, Quoc V. Le:
MixConv: Mixed Depthwise Convolutional Kernels. CoRR abs/1907.09595 (2019) - [i5]Mingxing Tan, Ruoming Pang, Quoc V. Le:
EfficientDet: Scalable and Efficient Object Detection. CoRR abs/1911.09070 (2019) - [i4]Yu Liu, Xuhui Jia, Mingxing Tan, Raviteja Vemulapalli, Yukun Zhu, Bradley Green, Xiaogang Wang:
Search to Distill: Pearls are Everywhere but not the Eyes. CoRR abs/1911.09074 (2019) - [i3]Cihang Xie, Mingxing Tan, Boqing Gong, Jiang Wang, Alan L. Yuille, Quoc V. Le:
Adversarial Examples Improve Image Recognition. CoRR abs/1911.09665 (2019) - [i2]Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Golnaz Ghiasi, Mingxing Tan, Yin Cui, Quoc V. Le, Xiaodan Song:
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization. CoRR abs/1912.05027 (2019) - 2018
- [i1]Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Quoc V. Le:
MnasNet: Platform-Aware Neural Architecture Search for Mobile. CoRR abs/1807.11626 (2018) - 2017
- [j1]Gai Liu
Architecture and Synthesis for Area-Efficient Pipelining of Irregular Loop Nests. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 36(11): 1817-1830 (2017) - 2015
- [c11]Ritchie Zhao, Mingxing Tan, Steve Dai, Zhiru Zhang
Area-efficient pipelining for FPGA-targeted high-level synthesis. DAC 2015: 157:1-157:6 - [c10]Mingxing Tan, Steve Dai, Udit Gupta, Zhiru Zhang
Mapping-Aware Constrained Scheduling for LUT-Based FPGAs. FPGA 2015: 190-199 - [c9]Mingxing Tan, Gai Liu, Ritchie Zhao, Steve Dai, Zhiru Zhang
ElasticFlow: A Complexity-Effective Approach for Pipelining Irregular Loop Nests. ICCAD 2015: 78-85 - [c8]Mingkai Huang, Dan He, Xianhua Liu
An Energy-Efficient Branch Prediction with Grouped Global History. ICPP 2015: 140-149 - 2014
- [c7]Steve Dai, Mingxing Tan, Kecheng Hao, Zhiru Zhang
Flushing-Enabled Loop Pipelining for High-Level Synthesis. DAC 2014: 76:1-76:6 - [c6]Mingxing Tan, Bin Liu, Steve Dai, Zhiru Zhang
Multithreaded pipeline synthesis for data-parallel kernels. ICCAD 2014: 718-725 - [c5]Gai Liu, Ye Tao, Mingxing Tan, Zhiru Zhang
CASA: correlation-aware speculative adders. ISLPED 2014: 189-194 - [c4]Shreesha Srinath, Berkin Ilbeyi, Mingxing Tan, Gai Liu, Zhiru Zhang
Architectural Specialization for Inter-Iteration Loop Dependence Patterns. MICRO 2014: 583-595 - 2012
- [c3]Mingxing Tan, Xianhua Liu, Zichao Xie, Dong Tong, Xu Cheng:
Energy-efficient branch prediction with Compiler-guided History Stack. DATE 2012: 449-454 - [c2]Mingxing Tan, Xianhua Liu
CVP: an energy-efficient indirect branch prediction with compiler-guided value pattern. ICS 2012: 111-120 - 2010
- [c1]Jiyu Zhang, Zhiru Zhang
Bit-level optimization for high-level synthesis and FPGA-based acceleration. FPGA 2010: 59-68
Coauthor Index
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