| 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 | 2084 | 2023 |
| Classification and clustering via dictionary learning with structured incoherence and shared features I Ramirez, P Sprechmann, G Sapiro Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on …, 2010 | 960 | 2010 |
| Agent57: Outperforming the atari human benchmark AP Badia, B Piot, S Kapturowski, P Sprechmann, A Vitvitskyi, ZD Guo, ... International conference on machine learning, 507-517, 2020 | 698 | 2020 |
| Accelerating eulerian fluid simulation with convolutional networks J Tompson, K Schlachter, P Sprechmann, K Perlin International conference on machine learning, 3424-3433, 2017 | 697 | 2017 |
| 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 | 617 | 2024 |
| Disentangling factors of variation in deep representation using adversarial training MF Mathieu, JJ Zhao, J Zhao, A Ramesh, P Sprechmann, Y LeCun Advances in neural information processing systems 29, 2016 | 568 | 2016 |
| Super-resolution with deep convolutional sufficient statistics J Bruna, P Sprechmann, Y LeCun arXiv preprint arXiv:1511.05666, 2015 | 421 | 2015 |
| Never give up: Learning directed exploration strategies A Puigdomènech Badia, P Sprechmann, A Vitvitskyi, D Guo, B Piot, ... arXiv e-prints, arXiv: 2002.06038, 2020 | 366* | 2020 |
| Learning efficient sparse and low rank models P Sprechmann, AM Bronstein, G Sapiro IEEE transactions on pattern analysis and machine intelligence 37 (9), 1821-1833, 2015 | 224 | 2015 |
| C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework P Sprechmann, I Ramírez, G Sapiro, YC Eldar Signal Processing, IEEE Transactions on 59 (9), 4183-4198, 2011 | 210 | 2011 |
| Classification and 3D averaging with missing wedge correction in biological electron tomography A Bartesaghi, P Sprechmann, J Liu, G Randall, G Sapiro, S Subramaniam Journal of structural biology 162 (3), 436-450, 2008 | 196 | 2008 |
| Dictionary learning and sparse coding for unsupervised clustering P Sprechmann, G Sapiro 2010 IEEE international conference on acoustics, speech and signal …, 2010 | 167 | 2010 |
| Sparse modeling of intrinsic correspondences J Pokrass, AM Bronstein, MM Bronstein, P Sprechmann, G Sapiro Computer graphics forum 32 (2pt4), 459-468, 2013 | 131 | 2013 |
| Memory-based parameter adaptation P Sprechmann, SM Jayakumar, JW Rae, A Pritzel, AP Badia, B Uria, ... arXiv preprint arXiv:1802.10542, 2018 | 118 | 2018 |
| Game Plan: What AI can do for Football, and What Football can do for AI K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ... Journal of Artificial Intelligence Research 71, 41-88, 2021 | 108 | 2021 |
| Meta-learning of sequential strategies PA Ortega, JX Wang, M Rowland, T Genewein, Z Kurth-Nelson, ... arXiv preprint arXiv:1905.03030, 2019 | 97 | 2019 |
| Supervised sparse analysis and synthesis operators P Sprechmann, R Litman, T Ben Yakar, AM Bronstein, G Sapiro Advances in Neural Information Processing Systems 26, 2013 | 80 | 2013 |
| Real-time Online Singing Voice Separation from Monaural Recordings Using Robust Low-rank Modeling. P Sprechmann, AM Bronstein, G Sapiro ISMIR, 67-72, 2012 | 74 | 2012 |
| Quantifying risk for anxiety disorders in preschool children: a machine learning approach KLH Carpenter, P Sprechmann, R Calderbank, G Sapiro, HL Egger PloS one 11 (11), e0165524, 2016 | 60 | 2016 |
| Collaborative hierarchical sparse modeling P Sprechmann, I Ramirez, G Sapiro, Y Eldar 2010 44th Annual Conference on Information Sciences and Systems (CISS), 1-6, 2010 | 54 | 2010 |
Guillermo SapiroAugustine Family Professor, Princeton University; Distinguished Engineer, Health AI Lead, Apple.在 princeton.edu 的電子郵件地址已通過驗證