| The convergence of sparsified gradient methods D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli Advances in Neural Information Processing Systems 31, 2018 | 457 | 2018 |
| SparCML: High-performance sparse communication for machine learning C Renggli, S Ashkboos, M Aghagolzadeh, D Alistarh, T Hoefler Proceedings of the International Conference for High Performance Computing …, 2019 | 121 | 2019 |
| Distributed learning over unreliable networks C Yu, H Tang, C Renggli, S Kassing, A Singla, D Alistarh, C Zhang, J Liu International Conference on Machine Learning, 7202-7212, 2019 | 59 | 2019 |
| A data quality-driven view of mlops C Renggli, L Rimanic, NM Gürel, B Karlaš, W Wu, C Zhang arXiv preprint arXiv:2102.07750, 2021 | 55 | 2021 |
| Scalable transfer learning with expert models J Puigcerver, C Riquelme, B Mustafa, C Renggli, AS Pinto, S Gelly, ... arXiv preprint arXiv:2009.13239, 2020 | 45 | 2020 |
| Continuous integration of machine learning models with ease. ml/ci: Towards a rigorous yet practical treatment C Renggli, B Karlaš, B Ding, F Liu, K Schawinski, W Wu, C Zhang Proceedings of Machine Learning and Systems 1, 322-333, 2019 | 42 | 2019 |
| Building continuous integration services for machine learning B Karlaš, M Interlandi, C Renggli, W Wu, C Zhang, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 27 | 2020 |
| Learning to merge tokens in vision transformers C Renggli, AS Pinto, N Houlsby, B Mustafa, J Puigcerver, C Riquelme arXiv preprint arXiv:2202.12015, 2022 | 23 | 2022 |
| Ease. ml: a lifecycle management system for machine learning L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... Proceedings of the Annual Conference on Innovative Data Systems Research …, 2021 | 18 | 2021 |
| Decoding EEG brain activity for multi-modal natural language processing N Hollenstein, C Renggli, B Glaus, M Barrett, M Troendle, N Langer, ... Frontiers in Human Neuroscience, 378, 2021 | 17 | 2021 |
| Which model to transfer? finding the needle in the growing haystack C Renggli, AS Pinto, L Rimanic, J Puigcerver, C Riquelme, C Zhang, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022 | 13 | 2022 |
| Ease. ml/ci and ease. ml/meter in action: Towards data management for statistical generalization C Renggli, FA Hubis, B Karlaš, K Schawinski, W Wu, C Zhang Proceedings of the VLDB Endowment 12 (12), 1962-1965, 2019 | 11 | 2019 |
| On convergence of nearest neighbor classifiers over feature transformations L Rimanic, C Renggli, B Li, C Zhang Advances in Neural Information Processing Systems 33, 12521-12532, 2020 | 10 | 2020 |
| Lossy image compression with recurrent neural networks: from human perceived visual quality to classification accuracy M Weber, C Renggli, H Grabner, C Zhang arXiv preprint arXiv:1910.03472, 2019 | 10 | 2019 |
| Speeding up percolator JT Halloran, H Zhang, K Kara, C Renggli, M The, C Zhang, DM Rocke, ... Journal of proteome research 18 (9), 3353-3359, 2019 | 8 | 2019 |
| Observer dependent lossy image compression M Weber, C Renggli, H Grabner, C Zhang Pattern Recognition: 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen …, 2021 | 7 | 2021 |
| Ease. ml/snoopy in action: Towards automatic feasibility analysis for machine learning application development C Renggli, L Rimanic, L Kolar, W Wu, C Zhang Proceedings of the VLDB Endowment 13 (12), 2837-2840, 2020 | 5 | 2020 |
| Evaluating Bayes error estimators on real-world datasets with FeeBee C Renggli, L Rimanic, N Hollenstein, C Zhang arXiv preprint arXiv:2108.13034, 2021 | 4 | 2021 |
| Ease. ML: A lifecycle management system for MLDev and MLOps L Aguilar, D Dao, S Gan, NM Gurel, N Hollenstein, J Jiang, B Karlas, ... Proc. of Innovative Data Systems Research, 2021 | 4 | 2021 |
| Dynamic human evaluation for relative model comparisons T Thorleiksdóttir, C Renggli, N Hollenstein, C Zhang arXiv preprint arXiv:2112.08048, 2021 | 2 | 2021 |
Ce ZhangTogether AIVerified email at together.xyz
