{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 | 18847 | 2016 |

TensorFlow: Large-scale machine learning on heterogeneous systems M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... | 13115 | 2015 |

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 | 8690 | 2016 |

Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv 2016 M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467, 2019 | 899 | 2019 |

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 | 458 | 2004 |

TensorFlow: large-scale machine learning on heterogeneous distributed systems (2016) M Abadi, A Agarwal, P Barham, E Brevdo, Z Chen, C Citro, GS Corrado, ... arXiv preprint arXiv:1603.04467 52, 2015 | 326 | 2015 |

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 | 240 | 2005 |

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 | 224 | 2006 |

A quantized-diffusion model for rendering translucent materials E d'Eon, G Irving ACM transactions on graphics (TOG) 30 (4), 1-14, 2011 | 165 | 2011 |

Hybrid simulation of deformable solids E Sifakis, T Shinar, G Irving, R Fedkiw Proceedings of the 2007 ACM SIGGRAPH/Eurographics symposium on Computer …, 2007 | 164 | 2007 |

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 | 154 | 2008 |

Deep network guided proof search S Loos, G Irving, C Szegedy, C Kaliszyk arXiv preprint arXiv:1701.06972, 2017 | 152 | 2017 |

Volume conserving finite element simulations of deformable models G Irving, C Schroeder, R Fedkiw ACM Transactions on Graphics (TOG) 26 (3), 13-es, 2007 | 152 | 2007 |

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 | 147 | 2018 |

Melting and burning solids into liquids and gases F Losasso, G Irving, E Guendelman, R Fedkiw IEEE Transactions on Visualization and Computer Graphics 12 (3), 343-352, 2006 | 141 | 2006 |

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 | 140 | 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, ... USENIX Association, TensorFlow: a system for large-scale machine learning …, 2016 | 124 | 2016 |

Deepmath-deep sequence models for premise selection G Irving, C Szegedy, AA Alemi, N Eén, F Chollet, J Urban Advances in neural information processing systems 29, 2016 | 110 | 2016 |

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 | 97 | 2021 |

DeepMath-deep sequence models for premise selection AA Alemi, F Chollet, N Eén, G Irving, C Szegedy, J Urban arXiv preprint arXiv:1606.04442, 2016 | 88 | 2016 |