| 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 | 26028 | 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 | 1206 | 2018 |
| Transformers learn in-context by gradient descent J Von Oswald, E Niklasson, E Randazzo, J Sacramento, A Mordvintsev, ... International Conference on Machine Learning, 35151-35174, 2023 | 394 | 2023 |
| Description and first application of a new technique to measure the gravitational mass of antihydrogen AE Charman Nature communications 4 (1), 1785, 2013 | 304 | 2013 |
| Cyclegan, a master of steganography C Chu, A Zhmoginov, M Sandler arXiv preprint arXiv:1712.02950, 2017 | 258 | 2017 |
| MobileNetV2: Inverted Residuals and Linear Bottlenecks. arXiv 2018 M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen arXiv preprint arXiv:1801.04381, 1801 | 232 | 1801 |
| 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 | 199 | 2018 |
| The power of sparsity in convolutional neural networks S Changpinyo, M Sandler, A Zhmoginov arXiv preprint arXiv:1702.06257, 2017 | 168 | 2017 |
| An experimental limit on the charge of antihydrogen C Amole, MD Ashkezari, M Baquero-Ruiz, W Bertsche, E Butler, A Capra, ... Nature communications 5 (1), 3955, 2014 | 94 | 2014 |
| An improved limit on the charge of antihydrogen from stochastic acceleration M Ahmadi, M Baquero-Ruiz, W Bertsche, E Butler, A Capra, C Carruth, ... Nature 529 (7586), 373-376, 2016 | 81 | 2016 |
| Antimatter interferometry for gravity measurements P Hamilton, A Zhmoginov, F Robicheaux, J Fajans, JS Wurtele, H Müller Physical review letters 112 (12), 121102, 2014 | 77 | 2014 |
| Inverting face embeddings with convolutional neural networks A Zhmoginov, M Sandler arXiv preprint arXiv:1606.04189, 2016 | 76 | 2016 |
| Hypertransformer: Model generation for supervised and semi-supervised few-shot learning A Zhmoginov, M Sandler, M Vladymyrov International Conference on Machine Learning, 27075-27098, 2022 | 75 | 2022 |
| K for the price of 1: Parameter-efficient multi-task and transfer learning PK Mudrakarta, M Sandler, A Zhmoginov, A Howard arXiv preprint arXiv:1810.10703, 2018 | 73 | 2018 |
| Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. CoRR abs/1801.04381 (2018) M Sandler, AG Howard, M Zhu, A Zhmoginov, L Chen arXiv preprint arXiv:1801.04381, 1801 | 54 | 1801 |
| Large-scale generative data-free distillation L Luo, M Sandler, Z Lin, A Zhmoginov, A Howard arXiv preprint arXiv:2012.05578, 2020 | 52 | 2020 |
| Mobilenetv2: The next generation of on-device computer vision networks M Sandler, A Howard, M Zhu, A Zhmoginov, LC Chen Google AI Blog 3, 2018 | 51 | 2018 |
| Fine-tuning image transformers using learnable memory M Sandler, A Zhmoginov, M Vladymyrov, A Jackson Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 46 | 2022 |
| Non-discriminative data or weak model? on the relative importance of data and model resolution M Sandler, J Baccash, A Zhmoginov, A Howard Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 39 | 2019 |
| Experimental and computational study of the injection of antiprotons into a positron plasma for antihydrogen production C Amole, MD Ashkezari, M Baquero-Ruiz, W Bertsche, E Butler, A Capra, ... Physics of Plasmas 20 (4), 2013 | 32 | 2013 |
Mark SandlerGoogleVahvistettu sähköpostiosoite verkkotunnuksessa google.com
