| The convergence of sparsified gradient methods D Alistarh, T Hoefler, M Johansson, S Khirirat, N Konstantinov, C Renggli arXiv preprint arXiv:1809.10505, 2018 | 151 | 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 | 49 | 2019 |
| 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 arXiv preprint arXiv:1903.00278, 2019 | 19 | 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 | 17 | 2019 |
| Scalable transfer learning with expert models J Puigcerver, C Riquelme, B Mustafa, C Renggli, AS Pinto, S Gelly, ... arXiv preprint arXiv:2009.13239, 2020 | 6 | 2020 |
| 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 | 5 | 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 | 4 | 2020 |
| 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 | 3 | 2020 |
| Continuous Integration of Machine Learning Models: A Rigorous Yet Practical Treatment C Renggli, B Karlas, B Ding, F Liu, K Schawinski, W Wu, C Zhang Proceedings of SysML 2019, 2019 | 3 | 2019 |
| 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 | 2 | 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 | 2 | 2019 |
| On Automatic Feasibility Study for Machine Learning Application Development with ease. ml/snoopy C Renggli, L Rimanic, L Kolar, N Hollenstein, W Wu, C Zhang arXiv preprint arXiv:2010.08410, 2020 | 1 | 2020 |
| On Convergence of Nearest Neighbor Classifiers over Feature Transformations L Rimanic, C Renggli, B Li, C Zhang arXiv preprint arXiv:2010.07765, 2020 | 1 | 2020 |
| Decoding EEG Brain Activity for Multi-Modal Natural Language Processing N Hollenstein, C Renggli, B Glaus, M Barrett, M Troendle, N Langer, ... arXiv preprint arXiv:2102.08655, 2021 | | 2021 |
| 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 | | 2021 |
| Ease. ML: A Lifecycle Management System for Machine Learning L Aguilar Melgar, D Dao, S Gan, NM Gürel, N Hollenstein, J Jiang, ... CIDR, 2021 | | 2021 |
| Which Model to Transfer? Finding the Needle in the Growing Haystack C Renggli, AS Pinto, L Rimanic, J Puigcerver, C Riquelme, C Zhang, ... arXiv preprint arXiv:2010.06402, 2020 | | 2020 |
| Observer dependent lossy image compression M Weber, C Renggli, H Grabner, C Zhang 42nd German Conference on Pattern Recognition (DAGM-GCPR), virtual, 28 …, 2020 | | 2020 |
| Ease. ML: A Lifecycle Management System for Machine Learning L Aguilar, D Dao, S Gan, NM Gurel, N Hollenstein, J Jiang, B Karlas, ... | | 2020 |
| Distributed Learning over Unreliable Networks Download PDF C Yu, H Tang, C Renggli, S Kassing, A Singla, D Alistarh, C Zhang, J Liu | | |
Ce ZhangETH ZurichVerified email at inf.ethz.ch
