| Ensemble Distillation for Robust Model Fusion in Federated Learning T Lin*, L Kong*, SU Stich, M Jaggi NeurIPS 2020 - Advances in Neural Information Processing Systems, 2020, 2020 | 986 | 2020 |
| Don't Use Large Mini-Batches, Use Local SGD T Lin, SU Stich, KK Patel, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 482 | 2020 |
| Fog orchestration for internet of things services Z Wen, R Yang, P Garraghan, T Lin, J Xu, M Rovatsos IEEE Internet Computing 21 (2), 16-24, 2017 | 413 | 2017 |
| Decentralized Deep Learning with Arbitrary Communication Compression A Koloskova*, T Lin*, SU Stich, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 238 | 2020 |
| Dynamic Model Pruning with Feedback T Lin, SU Stich, L Barba, D Dmitriev, M Jaggi ICLR 2020 - International Conference on Learning Representations, 2020 | 230 | 2020 |
| Exploring interpretable LSTM neural networks over multi-variable data T Guo, T Lin, N Antulov-Fantulin ICML 2019 - International Conference on Machine Learning, 2494-2504, 2019 | 210 | 2019 |
| Hybrid Neural Networks for Learning the Trend in Time Series T Lin*, T Guo*, K Aberer IJCAI 2017 - Proceedings of the Twenty-Sixth International Joint Conference …, 2017 | 184 | 2017 |
| Training DNNs with Hybrid Block Floating Point M Drumond, T Lin, M Jaggi, B Falsafi NeurIPS 2018 - Advances in Neural Information Processing Systems, 2018, 2018 | 121 | 2018 |
| Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data T Lin, SP Karimireddy, SU Stich, M Jaggi ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021 | 97 | 2021 |
| Masking as an Efficient Alternative to Finetuning for Pretrained Language Models M Zhao*, T Lin*, M Jaggi, H Schütze EMNLP 2020 - Empirical Methods in Natural Language Processing, 2020 | 96 | 2020 |
| An Improved Analysis of Gradient Tracking for Decentralized Machine Learning A Koloskova, T Lin, SU Stich NeurIPS 2021 - Advances in Neural Information Processing Systems, 2021 34, 2021 | 93 | 2021 |
| On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them C Liu, M Salzmann, T Lin, R Tomioka, S Süsstrunk NeurIPS 2020 - Advances in Neural Information Processing Systems, 2020, 2020 | 83 | 2020 |
| Consensus Control for Decentralized Deep Learning L Kong*, T Lin*, A Koloskova, M Jaggi, SU Stich ICML 2021 - Proceedings of the 38th International Conference on Machine Learning, 2021 | 82 | 2021 |
| RelaySum for Decentralized Deep Learning on Heterogeneous Data T Vogels*, L He*, A Koloskova, T Lin, SP Karimireddy, SU Stich, M Jaggi NeurIPS 2021 - Advances in Neural Information Processing Systems, 2021, 2021 | 63 | 2021 |
| An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function H Li*, X Wang*, D Rukina, Q Huang, T Lin, V Sorrentino, H Zhang, ... Cell systems 6 (1), 90-102. e4, 2018 | 57 | 2018 |
| An interpretable LSTM neural network for autoregressive exogenous model T Guo*, T Lin*, Y Lu ICLR 2018 Workshop, 2018 | 48 | 2018 |
| GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds Z Wen, T Lin, R Yang, S Ji, R Ranjan, A Romanovsky, C Lin, J Xu IEEE Transactions on Parallel and Distributed Systems 31 (1), 129-143, 2019 | 46 | 2019 |
| No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Z Li, X Shang, R He, T Lin, C Wu ICCV 2023 - International Conference on Computer Vision, 2023 | 45 | 2023 |
| Extrapolation for Large-batch Training in Deep Learning T Lin*, L Kong*, SU Stich, M Jaggi ICML 2020 - Proceedings of the 37th International Conference on Machine Learning, 2020 | 44 | 2020 |
| Revisiting Weighted Aggregation in Federated Learning with Neural Networks Z Li, T Lin, X Shang, C Wu ICML 2023 - International Conference on Machine Learning, 2023 | 43 | 2023 |
Martin JaggiEPFLVerified email at epfl.ch
