| Learning hierarchical features for scene labeling C Farabet, C Couprie, L Najman, Y LeCun IEEE transactions on pattern analysis and machine intelligence 35 (8), 1915-1929, 2012 | 3496 | 2012 |
| Convolutional networks and applications in vision Y LeCun, K Kavukcuoglu, C Farabet Proceedings of 2010 IEEE international symposium on circuits and systems …, 2010 | 2976 | 2010 |
| Torch7: A matlab-like environment for machine learning R Collobert, K Kavukcuoglu, C Farabet BigLearn, NIPS workshop, 2011 | 1983 | 2011 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1042 | 2023 |
| Indoor semantic segmentation using depth information C Couprie, C Farabet, L Najman, Y LeCun arXiv preprint arXiv:1301.3572, 2013 | 587 | 2013 |
| Neuflow: A runtime reconfigurable dataflow processor for vision C Farabet, B Martini, B Corda, P Akselrod, E Culurciello, Y LeCun CVPR 2011 workshops, 109-116, 2011 | 533 | 2011 |
| Cnp: An fpga-based processor for convolutional networks C Farabet, C Poulet, JY Han, Y LeCun 2009 International Conference on Field Programmable Logic and Applications …, 2009 | 501 | 2009 |
| Hardware accelerated convolutional neural networks for synthetic vision systems C Farabet, B Martini, P Akselrod, S Talay, Y LeCun, E Culurciello Proceedings of 2010 IEEE international symposium on circuits and systems …, 2010 | 398 | 2010 |
| Scene parsing with multiscale feature learning, purity trees, and optimal covers C Farabet, C Couprie, L Najman, Y LeCun arXiv preprint arXiv:1202.2160, 2012 | 263 | 2012 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 254 | 2024 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 196 | 2024 |
| Large-scale FPGA-based convolutional networks C Farabet, Y LeCun, K Kavukcuoglu, E Culurciello, B Martini, P Akselrod, ... Scaling up machine learning: parallel and distributed approaches 13 (3), 399-419, 2011 | 145 | 2011 |
| Scalable active learning for object detection E Haussmann, M Fenzi, K Chitta, J Ivanecky, H Xu, D Roy, A Mittel, ... 2020 IEEE intelligent vehicles symposium (iv), 1430-1435, 2020 | 135 | 2020 |
| NeuFlow: Dataflow vision processing system-on-a-chip PH Pham, D Jelaca, C Farabet, B Martini, Y LeCun, E Culurciello 2012 IEEE 55th International Midwest Symposium on Circuits and Systems …, 2012 | 134 | 2012 |
| Active learning for deep object detection via probabilistic modeling J Choi, I Elezi, HJ Lee, C Farabet, JM Alvarez Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 119 | 2021 |
| Runtime reconfigurable dataflow processor with multi-port memory access module C Farabet, Y LeCun US Patent 10,078,620, 2018 | 99 | 2018 |
| Comparison between frame-constrained fix-pixel-value and frame-free spiking-dynamic-pixel convnets for visual processing C Farabet, R Paz, J Pérez-Carrasco, C Zamarreño-Ramos, ... Frontiers in neuroscience 6, 32, 2012 | 85 | 2012 |
| An fpga-based stream processor for embedded real-time vision with convolutional networks C Farabet, C Poulet, Y LeCun 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV …, 2009 | 82 | 2009 |
| Tracking with deep neural networks J Jin, A Dundar, J Bates, C Farabet, E Culurciello 2013 47th annual conference on information sciences and systems (CISS), 1-5, 2013 | 61 | 2013 |
| Levels of AGI: Operationalizing Progress on the Path to AGI MR Morris, J Sohl-Dickstein, N Fiedel, T Warkentin, A Dafoe, A Faust, ... arXiv preprint arXiv:2311.02462, 2023 | 53 | 2023 |
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
