| Squad: 100,000+ questions for machine comprehension of text P Rajpurkar, J Zhang, K Lopyrev, P Liang Conference on Empirical Methods in Natural Language Processing, 2016 | 2705 | 2016 |
| DAWNBench: An End-to-End Deep Learning Benchmark and Competition C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ... SysML conference, 2018 | 166 | 2018 |
| Estimating the 3D Layout of Indoor Scenes and Its Clutter from Depth Sensors J Zhang, K Chen, A Schwing, R Urtasun International Conference on Computer Vision, 2013 | 81 | 2013 |
| YellowFin and the Art of Momentum Tuning J Zhang, I Mitliagkas SysML Conference, 2019 | 77 | 2019 |
| Deep learning at 15pf: supervised and semi-supervised classification for scientific data T Kurth, J Zhang, N Satish, E Racah, I Mitliagkas, MMA Patwary, T Malas, ... Proceedings of the International Conference for High Performance Computing …, 2017 | 66 | 2017 |
| High-accuracy low-precision training C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ... arXiv preprint arXiv:1803.03383, 2018 | 65 | 2018 |
| Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016 | 57 | 2016 |
| Analysis of the Time-To-Accuracy Metric and Entries in the DAWNBench Deep Learning Benchmark C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ... Workshop on Systems for ML and Open Source Software at NeurIPS 2018, 2018 | 43* | 2018 |
| Higher-Order Inference for Multi-class Log-supermodular Models J Zhang, J Djolonga, A Krause International Conference on Computer Vision, 2015 | 27 | 2015 |
| Low-memory neural network training: A technical report NS Sohoni, CR Aberger, M Leszczynski, J Zhang, C Ré arXiv preprint arXiv:1904.10631, 2019 | 23 | 2019 |
| Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation J Zhang, A May, T Dao, C Ré International Conference on Artificial Intelligence and Statistics, 2018 | 15 | 2018 |
| Contextual Embeddings: When Are They Worth It? S Arora, A May, J Zhang, C Ré arXiv preprint arXiv:2005.09117, 2020 | 9 | 2020 |
| On the downstream performance of compressed word embeddings A May, J Zhang, T Dao, C Ré Advances in neural information processing systems 32, 11782, 2019 | 8 | 2019 |
| Pipemare: Asynchronous pipeline parallel dnn training B Yang, J Zhang, J Li, C Ré, CR Aberger, C De Sa arXiv preprint arXiv:1910.05124, 2019 | 7 | 2019 |
| Message Passing Inference for Large Scale Graphical Models with High Order Potentials J Zhang, A Schwing, U Raquel Advances in Neural Information Processing Systems, 2015 | 7 | 2015 |
| Training with Low-precision Embedding Tables J Zhang, J Yang, H Yuen | 6 | 2018 |
| Understanding the downstream instability of word embeddings M Leszczynski, A May, J Zhang, S Wu, CR Aberger, C Ré arXiv preprint arXiv:2003.04983, 2020 | 3 | 2020 |
| YellowFin: Adaptive optimization for (A) synchronous systems J Zhang, I Mitliagkas SysML Conference, 2018 | 2 | 2018 |
| Revisiting BFloat16 Training P Zamirai, J Zhang, CR Aberger, C De Sa arXiv preprint arXiv:2010.06192, 2020 | 1 | 2020 |
| Exploring the Utility of Developer Exhaust J Zhang, M Lam, S Wang, P Varma, L Nardi, K Olukotun, C Ré Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018 | 1 | 2018 |
Christopher RéComputer Science, Stanford UniversityBestätigte E-Mail-Adresse bei cs.stanford.edu
