| 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 | 6022 | 2016 |
| From captions to visual concepts and back H Fang, S Gupta, F Iandola, RK Srivastava, L Deng, P Dollįr, J Gao, X He, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1363 | 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 | 603 | 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 | 521 | 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 | 487 | 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 | 322 | 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 | 273 | 2013 |
| How to scale distributed deep learning? PH Jin, Q Yuan, F Iandola, K Keutzer NIPS Workshops, 2016 | 123 | 2016 |
| SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and< 0.5 MB model size. arXiv 2016 FN Iandola, S Han, MW Moskewicz, K Ashraf, WJ Dally, K Keutzer arXiv preprint arXiv:1602.07360, 0 | 122 | |
| DeepLogo: Hitting logo recognition with the deep neural network hammer FN Iandola, A Shen, P Gao, K Keutzer arXiv preprint arXiv:1510.02131, 2015 | 82 | 2015 |
| Shallow networks for high-accuracy road object-detection K Ashraf, B Wu, FN Iandola, MW Moskewicz, K Keutzer arXiv preprint arXiv:1606.01561, 2016 | 59 | 2016 |
| 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 | 55 | 2017 |
| SqueezeBERT: What can computer vision teach NLP about efficient neural networks? FN Iandola, AE Shaw, R Krishna, KW Keutzer EMNLP SustaiNLP Workshop, 2020 | 50 | 2020 |
| Keynote: small neural nets are beautiful: enabling embedded systems with small deep-neural-network architectures F Iandola, K Keutzer 2017 International Conference on Hardware/Software Codesign and System …, 2017 | 49 | 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 | 40 | 2019 |
| Optimal load management system for aircraft electric power distribution M Maasoumy, P Nuzzo, F Iandola, M Kamgarpour, ... 52nd IEEE Conference on Decision and Control, 2939-2945, 2013 | 40 | 2013 |
| Communication-minimizing 2D convolution in GPU registers FN Iandola, D Sheffield, MJ Anderson, PM Phothilimthana, K Keutzer 2013 IEEE International Conference on Image Processing, 2116-2120, 2013 | 37 | 2013 |
| 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 | 29 | 2021 |
| Audio-based multimedia event detection with DNNs and sparse sampling K Ashraf, B Elizalde, F Iandola, M Moskewicz, J Bernd, G Friedland, ... Proceedings of the 5th ACM on International Conference on Multimedia …, 2015 | 25 | 2015 |
| Exploring the design space of deep convolutional neural networks at large scale F Iandola arXiv preprint arXiv:1612.06519, 2016 | 24 | 2016 |
Mehdi MaasoumyPhD, University of California, BerkeleyVerified email at berkeley.edu
