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ORCIDAndrew G. Howard
Person information
- affiliation: Google Inc., Mountain View, CA, USA
- affiliation: Columbia University, Department of Computer Science, New York, NY, USA
Other persons with the same name
- Andrew Howard — disambiguation page
- Andrew Howard 0001 — California Institute of Technology, Jet Propulsion Laboratory, Pasadena, CA, USA (and 1 more)
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Journal Articles
- 2019
[j2]Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen
, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong-gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo:
Low-Power Computer Vision: Status, Challenges, and Opportunities. IEEE J. Emerg. Sel. Topics Circuits Syst. 9(2): 411-421 (2019)- 2004
- [j1]Tony Jebara, Risi Kondor, Andrew G. Howard:
Probability Product Kernels. J. Mach. Learn. Res. 5: 819-844 (2004)
Conference and Workshop Papers
- 2023
- [c21]Shuyang Sun, Weijun Wang, Andrew G. Howard, Qihang Yu, Philip H. S. Torr, Liang-Chieh Chen:
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation. NeurIPS 2023 - 2022
- [c20]Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge J. Belongie, Andrew G. Howard, Oisin Mac Aodha:
On Label Granularity and Object Localization. ECCV (10) 2022: 604-620 - 2021
- [c19]Grace Chu, Okan Arikan, Gabriel Bender, Weijun Wang, Achille Brighton, Pieter-Jan Kindermans, Hanxiao Liu, Berkin Akin, Suyog Gupta, Andrew Howard:
Discovering Multi-Hardware Mobile Models via Architecture Search. CVPR Workshops 2021: 3022-3031 - [c18]Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka:
BasisNet: Two-Stage Model Synthesis for Efficient Inference. CVPR Workshops 2021: 3081-3090 - [c17]Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew G. Howard, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar, Feng Yang:
Multi-path Neural Networks for On-device Multi-domain Visual Classification. WACV 2021: 3018-3027 - 2020
- [c16]Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni:
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ACCV (6) 2020: 239-256 - [c15]Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew G. Howard, Marco Fornoni:
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. ECCV Workshops (1) 2020: 636-640 - 2019
- [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]Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard, Yang Song, Fernando Brucher, Thomas Leung, Hartwig Adam:
Geo-Aware Networks for Fine-Grained Recognition. ICCV Workshops 2019: 247-254 - [c11]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-Discriminative Data or Weak Model? On the Relative Importance of Data and Model Resolution. ICCV Workshops 2019: 1036-1044 - [c10]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning. ICLR (Poster) 2019 - 2018
- [c9]Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, Dmitry Kalenichenko:
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. CVPR 2018: 2704-2713 - [c8]Yin Cui, Yang Song, Chen Sun, Andrew Howard, Serge J. Belongie:
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018: 4109-4118 - [c7]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
MobileNetV2: Inverted Residuals and Linear Bottlenecks. CVPR 2018: 4510-4520 - [c6]Tien-Ju Yang, Andrew G. Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler, Vivienne Sze, Hartwig Adam:
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. ECCV (10) 2018: 289-304 - 2014
- [c5]Andrew G. Howard:
Some Improvements on Deep Convolutional Neural Network Based Image Classification. ICLR (Poster) 2014 - 2009
- [c4]Andrew G. Howard, Tony Jebara:
Transformation Learning Via Kernel Alignment. ICMLA 2009: 301-308 - 2007
- [c3]Andrew G. Howard, Tony Jebara:
Learning Monotonic Transformations for Classification. NIPS 2007: 681-688 - [c2]Risi Kondor, Andrew G. Howard, Tony Jebara:
Multi-object tracking with representations of the symmetric group. AISTATS 2007: 211-218 - 2004
- [c1]Andrew G. Howard, Tony Jebara:
Dynamical Systems Trees. UAI 2004: 260-267
Informal and Other Publications
- 2024
- [i25]Marina Neseem, Conor McCullough, Randy Hsin, Chas Leichner, Shan Li, In Suk Chong, Andrew G. Howard, Lukasz Lew, Sherief Reda, Ville-Mikko Rautio, Daniele Moro:
PikeLPN: Mitigating Overlooked Inefficiencies of Low-Precision Neural Networks. CoRR abs/2404.00103 (2024) - [i24]Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni, Shixin Luo, Fan Yang, Weijun Wang, Colby R. Banbury, Chengxi Ye, Berkin Akin, Vaibhav Aggarwal, Tenghui Zhu, Daniele Moro, Andrew Howard:
MobileNetV4 - Universal Models for the Mobile Ecosystem. CoRR abs/2404.10518 (2024) - [i23]Matt Schoenbauer, Daniele Moro, Lukasz Lew, Andrew Howard:
Custom Gradient Estimators are Straight-Through Estimators in Disguise. CoRR abs/2405.05171 (2024) - 2023
- [i22]Shuyang Sun, Weijun Wang, Qihang Yu, Andrew G. Howard, Philip H. S. Torr, Liang-Chieh Chen:
ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation. CoRR abs/2306.17319 (2023) - 2022
- [i21]Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang, Marco Fornoni, Pietro Perona, Serge J. Belongie, Andrew G. Howard, Oisin Mac Aodha:
On Label Granularity and Object Localization. CoRR abs/2207.10225 (2022) - 2021
- [i20]Keren Ye, Adriana Kovashka, Mark Sandler, Menglong Zhu, Andrew Howard, Marco Fornoni:
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection. CoRR abs/2101.01260 (2021) - [i19]Mingda Zhang, Chun-Te Chu, Andrey Zhmoginov, Andrew G. Howard, Brendan Jou, Yukun Zhu, Li Zhang, Rebecca Hwa, Adriana Kovashka:
BasisNet: Two-stage Model Synthesis for Efficient Inference. CoRR abs/2105.03014 (2021) - [i18]Marco Fornoni, Chaochao Yan, Liangchen Luo, Kimberly Wilber, Alex Stark, Yin Cui, Boqing Gong, Andrew G. Howard:
Bridging the Gap Between Object Detection and User Intent via Query-Modulation. CoRR abs/2106.10258 (2021) - [i17]Weijun Wang, Andrew Howard:
MOSAIC: Mobile Segmentation via decoding Aggregated Information and encoded Context. CoRR abs/2112.11623 (2021) - 2020
- [i16]Grace Chu, Okan Arikan, Gabriel Bender, Weijun Wang, Achille Brighton, Pieter-Jan Kindermans, Hanxiao Liu, Berkin Akin, Suyog Gupta, Andrew Howard:
Discovering Multi-Hardware Mobile Models via Architecture Search. CoRR abs/2008.08178 (2020) - [i15]Qifei Wang, Junjie Ke, Joshua Greaves, Grace Chu, Gabriel Bender, Luciano Sbaiz, Alec Go, Andrew Howard, Feng Yang, Ming-Hsuan Yang, Jeff Gilbert, Peyman Milanfar:
Multi-path Neural Networks for On-device Multi-domain Visual Classification. CoRR abs/2010.04904 (2020) - [i14]Liangchen Luo, Mark Sandler, Zi Lin, Andrey Zhmoginov, Andrew Howard:
Large-Scale Generative Data-Free Distillation. CoRR abs/2012.05578 (2020) - 2019
- [i13]Sergei Alyamkin, Matthew Ardi, Alexander C. Berg, Achille Brighton, Bo Chen, Yiran Chen, Hsin-Pai Cheng, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Abhinav Goel, Alexander Goncharenko, Xuyang Guo, Soonhoi Ha, Andrew Howard, Xiao Hu, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Jong-gook Ko, Alexander Kondratyev, Junhyeok Lee, Seungjae Lee, Suwoong Lee, Zichao Li, Zhiyu Liang, Juzheng Liu, Xin Liu, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Hong Hanh Nguyen, Eunbyung Park, Denis Repin, Liang Shen, Tao Sheng, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo:
Low-Power Computer Vision: Status, Challenges, Opportunities. CoRR abs/1904.07714 (2019) - [i12]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) - [i11]Grace Chu, Brian Potetz, Weijun Wang, Andrew Howard, Yang Song, Fernando Brucher, Thomas Leung, Hartwig Adam:
Geo-Aware Networks for Fine Grained Recognition. CoRR abs/1906.01737 (2019) - [i10]Aakanksha Chowdhery, Pete Warden, Jonathon Shlens, Andrew Howard, Rocky Rhodes:
Visual Wake Words Dataset. CoRR abs/1906.05721 (2019) - [i9]Mark Sandler, Jonathan Baccash, Andrey Zhmoginov, Andrew Howard:
Non-discriminative data or weak model? On the relative importance of data and model resolution. CoRR abs/1909.03205 (2019) - 2018
- [i8]Mark Sandler, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen:
Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation. CoRR abs/1801.04381 (2018) - [i7]Tien-Ju Yang, Andrew G. Howard, Bo Chen, Xiao Zhang, Alec Go, Vivienne Sze, Hartwig Adam:
NetAdapt: Platform-Aware Neural Network Adaptation for Mobile Applications. CoRR abs/1804.03230 (2018) - [i6]Yin Cui, Yang Song, Chen Sun, Andrew Howard, Serge J. Belongie:
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CoRR abs/1806.06193 (2018) - [i5]Sergei Alyamkin, Matthew Ardi, Achille Brighton, Alexander C. Berg, Yiran Chen, Hsin-Pai Cheng, Bo Chen, Zichen Fan, Chen Feng, Bo Fu, Kent Gauen, Jongkook Go, Alexander Goncharenko, Xuyang Guo, Hong Hanh Nguyen, Andrew Howard, Yuanjun Huang, Donghyun Kang, Jaeyoun Kim, Alexander Kondratyev, Seungjae Lee, Suwoong Lee, Junhyeok Lee, Zhiyu Liang, Xin Liu, Juzheng Liu, Zichao Li, Yang Lu, Yung-Hsiang Lu, Deeptanshu Malik, Eunbyung Park, Denis Repin, Tao Sheng, Liang Shen, Fei Sun, David Svitov, George K. Thiruvathukal, Baiwu Zhang, Jingchi Zhang, Xiaopeng Zhang, Shaojie Zhuo:
2018 Low-Power Image Recognition Challenge. CoRR abs/1810.01732 (2018) - [i4]Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew G. Howard:
K For The Price Of 1: Parameter Efficient Multi-task And Transfer Learning. CoRR abs/1810.10703 (2018) - 2017
- [i3]Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam:
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. CoRR abs/1704.04861 (2017) - [i2]Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew G. Howard, Hartwig Adam, Dmitry Kalenichenko:
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. CoRR abs/1712.05877 (2017) - 2012
- [i1]Andrew G. Howard, Tony Jebara:
Dynamical Systems Trees. CoRR abs/1207.4148 (2012)
Coauthor Index
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