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Tao LIN
Tao LIN
Westlake University
Verified email at westlake.edu.cn - Homepage
Title
Cited by
Cited by
Year
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
8392020
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
4702020
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
4002017
Decentralized Deep Learning with Arbitrary Communication Compression
A Koloskova*, T Lin*, SU Stich, M Jaggi
ICLR 2020 - International Conference on Learning Representations, 2020
2242020
Dynamic Model Pruning with Feedback
T Lin, SU Stich, L Barba, D Dmitriev, M Jaggi
ICLR 2020 - International Conference on Learning Representations, 2020
2102020
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
1962019
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
1812017
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
1192018
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
882020
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
872021
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
872021
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
772020
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
742021
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
592021
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
542018
An interpretable LSTM neural network for autoregressive exogenous model
T Guo*, T Lin*, Y Lu
ICLR 2018 Workshop, 2018
462018
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
432019
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
422020
Generalized Class Incremental Learning
F Mi*, L Kong*, T Lin, K Yu, B Faltings
CVPR Workshop 2020 - Proceedings of the IEEE/CVF Conference on Computer …, 2020
412020
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
352023
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