| Mobilenets: Efficient convolutional neural networks for mobile vision applications AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861, 2017 | 24374 | 2017 |
| Mobilenetv2: Inverted residuals and linear bottlenecks M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 21398 | 2018 |
| Searching for MobileNetV3 A Howard, M Sandler, G Chu, LC Chen, B Chen, M Tan, W Wang, Y Zhu, ... arXiv preprint arXiv:1905.02244, 2019 | 7013 | 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/CVF conference on computer vision and pattern …, 2019 | 3338 | 2019 |
| Quantization and training of neural networks for efficient integer-arithmetic-only inference B Jacob, S Kligys, B Chen, M Zhu, M Tang, A Howard, H Adam, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 3127 | 2018 |
| Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation A Howard, A Zhmoginov, LC Chen, M Sandler, M Zhu Proc. CVPR, 4510-4520, 2018 | 1023 | 2018 |
| Probability product kernels T Jebara, R Kondor, A Howard The Journal of Machine Learning Research 5, 819-844, 2004 | 674 | 2004 |
| Netadapt: Platform-aware neural network adaptation for mobile applications TJ Yang, A Howard, B Chen, X Zhang, A Go, M Sandler, V Sze, H Adam Proceedings of the European conference on computer vision (ECCV), 285-300, 2018 | 658 | 2018 |
| Some improvements on deep convolutional neural network based image classification AG Howard arXiv preprint arXiv:1312.5402, 2013 | 615 | 2013 |
| MobileNets: efficient convolutional neural networks for mobile vision applications (2017) AG Howard, M Zhu, B Chen, D Kalenichenko, W Wang, T Weyand, ... arXiv preprint arXiv:1704.04861 126, 2017 | 563 | 2017 |
| Large scale fine-grained categorization and domain-specific transfer learning Y Cui, Y Song, C Sun, A Howard, S Belongie Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 546 | 2018 |
| The unreasonable effectiveness of noisy data for fine-grained recognition J Krause, B Sapp, A Howard, H Zhou, A Toshev, T Duerig, J Philbin, ... Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 424 | 2016 |
| Proceedings of the IEEE conference on computer vision and pattern recognition M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 163 | 2018 |
| System and methods for adaptive model generation for detecting intrusions in computer systems A Honig, A Howard, E Eskin, SJ Stolfo US Patent 7,225,343, 2007 | 140 | 2007 |
| Inverted residuals and linear bottlenecks: Mobile networks for classification M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Detection and Segmentation, 4510-4520, 2018 | 125 | 2018 |
| Visual wake words dataset A Chowdhery, P Warden, J Shlens, A Howard, R Rhodes arXiv preprint arXiv:1906.05721, 2019 | 119 | 2019 |
| Multi-object tracking with representations of the symmetric group R Kondor, A Howard, T Jebara Artificial intelligence and statistics, 211-218, 2007 | 104 | 2007 |
| MobileNetV2: inverted residuals and linear bottlenecks (2018) M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen arXiv preprint arXiv:1801.04381, 1801 | 104 | 1801 |
| Adaptive model generation: An architecture for deployment of data mining-based intrusion detection systems A Honig, A Howard, E Eskin, S Stolfo Applications of data mining in computer security, 153-193, 2002 | 96 | 2002 |
| Systems and methods for adaptive model generation for detecting intrusions in computer systems A Honig, A Howard, E Eskin, SJ Stolfo US Patent 8,893,273, 2014 | 85 | 2014 |
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