Cedric Renggli
Cedric Renggli
Verified email at inf.ethz.ch - Homepage
Title
Cited by
Cited by
Year
The convergence of sparsified gradient methods
D Alistarh, T Hoefler, M Johansson, S Khirirat, N Konstantinov, C Renggli
arXiv preprint arXiv:1809.10505, 2018
1732018
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
512019
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
212019
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
202019
Scalable transfer learning with expert models
J Puigcerver, C Riquelme, B Mustafa, C Renggli, AS Pinto, S Gelly, ...
arXiv preprint arXiv:2009.13239, 2020
102020
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
62020
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
52019
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
32020
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
32019
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
32019
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
22019
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
12020
On Convergence of Nearest Neighbor Classifiers over Feature Transformations
L Rimanic, C Renggli, B Li, C Zhang
arXiv preprint arXiv:2010.07765, 2020
12020
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
12020
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
Ease. ML: A Lifecycle Management System for Machine Learning
L Aguilar, D Dao, S Gan, NM Gurel, N Hollenstein, J Jiang, B Karlas, ...
2020
Observer dependent lossy image compression
M Weber, C Renggli, H Grabner, C Zhang
Pattern Recognition 12544, 130, 2019
2019
Distributed Learning over Unreliable Networks Download PDF
C Yu, H Tang, C Renggli, S Kassing, A Singla, D Alistarh, C Zhang, J Liu
The system can't perform the operation now. Try again later.
Articles 1–20