| Tensorflow: Large-scale machine learning on heterogeneous distributed systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2016 | 27536 | 2016 |
| {TensorFlow}: a system for {Large-Scale} machine learning M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... 12th USENIX symposium on operating systems design and implementation (OSDI …, 2016 | 21759 | 2016 |
| TensorFlow: Large-scale machine learning on heterogeneous systems, software available from tensorflow. org (2015) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL https://www. tensorflow. org, 2015 | 696 | 2015 |
| Invertible finite elements for robust simulation of large deformation G Irving, J Teran, R Fedkiw Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer …, 2004 | 528 | 2004 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 468 | 2021 |
| Fine-tuning language models from human preferences DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... arXiv preprint arXiv:1909.08593, 2019 | 413 | 2019 |
| TensorFlow: large-scale machine learning on heterogeneous distributed systems. 2015 M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL http://download. tensorflow. org/paper/whitepaper2015. pdf, 12, 2015 | 354 | 2015 |
| Improving language models by retrieving from trillions of tokens S Borgeaud, A Mensch, J Hoffmann, T Cai, E Rutherford, K Millican, ... International conference on machine learning, 2206-2240, 2022 | 332 | 2022 |
| Ethical and social risks of harm from language models L Weidinger, J Mellor, M Rauh, C Griffin, J Uesato, PS Huang, M Cheng, ... arXiv preprint arXiv:2112.04359, 2021 | 320 | 2021 |
| Robust quasistatic finite elements and flesh simulation J Teran, E Sifakis, G Irving, R Fedkiw Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer …, 2005 | 277 | 2005 |
| TensorFlow: Large-scale machine learning on heterogeneous systems (2015), software available from tensorflow. org M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... URL https://www. tensorflow. org, 2019 | 247 | 2019 |
| 12th USENIX symposium on operating systems design and implementation (OSDI 16) M Abadi, P Barham, J Chen, Z Chen, A Davis, J Dean, M Devin, ... Savannah, GA, 265-283, 2016 | 241 | 2016 |
| Reward learning from human preferences and demonstrations in atari B Ibarz, J Leike, T Pohlen, G Irving, S Legg, D Amodei Advances in neural information processing systems 31, 2018 | 237 | 2018 |
| Efficient simulation of large bodies of water by coupling two and three dimensional techniques G Irving, E Guendelman, F Losasso, R Fedkiw ACM SIGGRAPH 2006 Papers, 805-811, 2006 | 232 | 2006 |
| Hybrid simulation of deformable solids E Sifakis, T Shinar, G Irving, R Fedkiw Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer …, 2007 | 182 | 2007 |
| A quantized-diffusion model for rendering translucent materials E d'Eon, G Irving ACM transactions on graphics (TOG) 30 (4), 1-14, 2011 | 178 | 2011 |
| Deep network guided proof search S Loos, G Irving, C Szegedy, C Kaliszyk arXiv preprint arXiv:1701.06972, 2017 | 171 | 2017 |
| Robust high-resolution cloth using parallelism, history-based collisions, and accurate friction A Selle, J Su, G Irving, R Fedkiw IEEE transactions on visualization and computer graphics 15 (2), 339-350, 2008 | 171 | 2008 |
| Volume conserving finite element simulations of deformable models G Irving, C Schroeder, R Fedkiw ACM Transactions on Graphics (TOG) 26 (3), 13-es, 2007 | 164 | 2007 |
| Tensorflow: A system for large-scale machine learning PB Mart'in Abadi, J Chen, Z Chen, A Davis, J Dean, M Devin, ... 12th USENIX symposium on operating systems design and implementation (OSDI …, 2016 | 161 | 2016 |
