| SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size FN Iandola, S Han, MW Moskewicz, K Ashraf, WJ Dally, K Keutzer arXiv preprint arXiv:1602.07360, 2016 | 10776 | 2016 |
| From captions to visual concepts and back H Fang, S Gupta, F Iandola, RK Srivastava, L Deng, P Dollár, J Gao, X He, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1703 | 2015 |
| Densenet: Implementing efficient convnet descriptor pyramids. F Iandola, M Moskewicz, S Karayev, R Girshick, T Darrell, K Keutzer arXiv preprint arXiv:1404.1869, 2014 | 1215 | 2014 |
| SqueezeDet: Unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving B Wu, F Iandola, PH Jin, K Keutzer Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 784 | 2017 |
| Deformable part models are convolutional neural networks R Girshick, F Iandola, T Darrell, J Malik Proceedings of the IEEE conference on Computer Vision and Pattern …, 2015 | 590 | 2015 |
| FireCaffe: near-linear acceleration of deep neural network training on compute clusters FN Iandola, MW Moskewicz, K Ashraf, K Keutzer Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 402 | 2016 |
| Deformable part descriptors for fine-grained recognition and attribute prediction N Zhang, R Farrell, F Iandola, T Darrell Proceedings of the IEEE International Conference on Computer Vision, 729-736, 2013 | 298 | 2013 |
| How to scale distributed deep learning? PH Jin, Q Yuan, F Iandola, K Keutzer NIPS Workshops, 2016 | 166 | 2016 |
| SqueezeBERT: What can computer vision teach NLP about efficient neural networks? FN Iandola, AE Shaw, R Krishna, KW Keutzer EMNLP SustaiNLP Workshop, 2020 | 134 | 2020 |
| Efficientsam: Leveraged masked image pretraining for efficient segment anything Y Xiong, B Varadarajan, L Wu, X Xiang, F Xiao, C Zhu, X Dai, D Wang, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 115 | 2024 |
| DeepLogo: Hitting logo recognition with the deep neural network hammer FN Iandola, A Shen, P Gao, K Keutzer arXiv preprint arXiv:1510.02131, 2015 | 100 | 2015 |
| Data synthesis for autonomous control systems FN Iandola, DB MacMillen, A Shen, HS Sidhu, PJ Jain US Patent 10,678,244, 2020 | 99 | 2020 |
| Discovery of semantic similarities between images and text J Gao, X He, S Gupta, GG Zweig, F Iandola, L Deng, H Fang, MA Mitchell, ... US Patent 9,836,671, 2017 | 93 | 2017 |
| SqueezeNAS: Fast neural architecture search for faster semantic segmentation A Shaw, D Hunter, F Iandola, S Sidhu Proceedings of the IEEE International Conference on Computer Vision …, 2019 | 91 | 2019 |
| SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and FN Iandola, S Han, MW Moskewicz, K Ashraf, WJ Dally, K Keutzer 5MB model size [J], 2016 | 85 | 2016 |
| Shallow networks for high-accuracy road object-detection K Ashraf, B Wu, FN Iandola, MW Moskewicz, K Keutzer arXiv preprint arXiv:1606.01561, 2016 | 67 | 2016 |
| Small neural nets are beautiful: enabling embedded systems with small deep-neural-network architectures F Iandola, K Keutzer Proceedings of the twelfth IEEE/ACM/IFIP international conference on …, 2017 | 65 | 2017 |
| Mobilellm: Optimizing sub-billion parameter language models for on-device use cases Z Liu, C Zhao, F Iandola, C Lai, Y Tian, I Fedorov, Y Xiong, E Chang, ... arXiv preprint arXiv:2402.14905, 2024 | 62 | 2024 |
| SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and< 0.5 MB model size. 2016 FN Iandola, S Han, MW Moskewicz, K Ashraf, WJ Dally, K Keutzer arXiv preprint arXiv:1602.07360, 0 | 54 | |
| Multi-channel sensor simulation for autonomous control systems FN Iandola, DB MacMillen, A Shen, HS Sidhu, DP Tomasello, RN Phadte, ... US Patent 11,157,014, 2021 | 51 | 2021 |
Kurt KeutzerProfessor of the Graduate School, EECS, University of California, BerkeleyVahvistettu sähköpostiosoite verkkotunnuksessa berkeley.edu
