| 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 | 8644 | 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 | 386 | 2018 |
| 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 | 141* | 2018 |
| High-accuracy low-precision training C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ... arXiv preprint arXiv:1803.03383, 2018 | 131 | 2018 |
| Pipemare: Asynchronous pipeline parallel dnn training B Yang, J Zhang, J Li, C Ré, C Aberger, C De Sa Proceedings of Machine Learning and Systems 3, 269-296, 2021 | 125 | 2021 |
| Parallel SGD: When does averaging help? J Zhang, C De Sa, I Mitliagkas, C Ré arXiv preprint arXiv:1606.07365, 2016 | 125 | 2016 |
| YellowFin and the Art of Momentum Tuning J Zhang, I Mitliagkas SysML Conference, 2019 | 117 | 2019 |
| 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 | 107 | 2013 |
| Low-memory neural network training: A technical report NS Sohoni, CR Aberger, M Leszczynski, J Zhang, C Ré arXiv preprint arXiv:1904.10631, 2019 | 105 | 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 | 96 | 2017 |
| Contextual embeddings: When are they worth it? S Arora, A May, J Zhang, C Ré arXiv preprint arXiv:2005.09117, 2020 | 81 | 2020 |
| On the tool manipulation capability of open-source large language models Q Xu, F Hong, B Li, C Hu, Z Chen, J Zhang arXiv preprint arXiv:2305.16504, 2023 | 65 | 2023 |
| 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 | 40 | 2018 |
| On the downstream performance of compressed word embeddings A May, J Zhang, T Dao, C Ré Advances in neural information processing systems 32, 2019 | 26 | 2019 |
| Revisiting BFfloat16 Training P Zamirai, J Zhang, CR Aberger, C De Sa | 23 | 2020 |
| Higher-Order Inference for Multi-class Log-supermodular Models J Zhang, J Djolonga, A Krause International Conference on Computer Vision, 2015 | 22 | 2015 |
| Training with Low-precision Embedding Tables J Zhang, J Yang, H Yuen | 19 | 2018 |
| Understanding the downstream instability of word embeddings M Leszczynski, A May, J Zhang, S Wu, C Aberger, C Ré Proceedings of Machine Learning and Systems 2, 262-290, 2020 | 16 | 2020 |
| 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 | 8 | 2015 |
| 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 UniversityVerified email at cs.stanford.edu
