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 | 3574 | 2012 |
Convolutional networks and applications in vision Y LeCun, K Kavukcuoglu, C Farabet Proceedings of 2010 IEEE international symposium on circuits and systems …, 2010 | 3085 | 2010 |
Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2249 | 2023 |
Torch7: A matlab-like environment for machine learning R Collobert, K Kavukcuoglu, C Farabet BigLearn, NIPS workshop, 2011 | 2025 | 2011 |
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 | 748 | 2024 |
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 724 | 2024 |
Indoor semantic segmentation using depth information C Couprie, C Farabet, L Najman, Y LeCun arXiv preprint arXiv:1301.3572, 2013 | 611 | 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 | 543 | 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 | 512 | 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 | 401 | 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 | 265 | 2012 |
Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 208 | 2024 |
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 | 150 | 2020 |
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 | 146 | 2021 |
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 | 146 | 2011 |
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 | 136 | 2012 |
Runtime reconfigurable dataflow processor with multi-port memory access module C Farabet, Y LeCun US Patent 10,078,620, 2018 | 103 | 2018 |
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 | 100 | 2023 |
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 | 87 | 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 | 83 | 2009 |