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ORCIDTao Lin 0004
Person information
- affiliation: EPFL, Lausanne, Switzerland
Other persons with the same name
- Tao Lin — disambiguation page
- Tao Lin 0001
![[0000-0003-1170-636X]](https://dblp.uni-trier.de/img/orcid-mark.12x12.png "0000-0003-1170-636X")
— Communication University of China, State Key Laboratory of Media Convergence and Communication, Beijing, China (and 2 more) - Tao Lin 0002 — University of Melbourne, Australia
- Tao Lin 0003 — Virginia Tech, Blacksburg, VA, USA
- Tao Lin 0005 — Tongji University, College of Electronics and Information Engineering, Shanghai, China (and 2 more)
- Tao Lin 0006 — Waseda University, Tokyo, Japan (and 1 more)
- Tao Lin 0007 — Iowa State University, Department of Electrical and Computer Engineering, Ames, IA, USA
- Tao Lin 0008 — Zhejiang University, College of Biosystems Engineering and Food Science, Hangzhou, China (and 2 more)
- Tao Lin 0009 — CAS Institute of Urban Environment, Key Lab of Urban Environment and Health, Xiamen, China
- Tao Lin 0010 — Amitive Inc., Redwood City, CA, USA
- Tao Lin 0011 — Shanghai Jiao Tong University, Welding Engineering Institute of Material Science and Engineering, China
- Tao Lin 0012 — University of Regina, Department of Computer Science, SK, Canada
- Tao Lin 0013 — Harvard University, Cambridge, MA, USA
Other persons with a similar name
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2020 – today
- 2024
[c17]Yuxuan Sun, Hao Wu, Chenglu Zhu, Sunyi Zheng, Qizi Chen, Kai Zhang, Yunlong Zhang, Dan Wan, Xiaoxiao Lan, Mengyue Zheng, Jingxiong Li, Xinheng Lyu, Tao Lin, Lin Yang:
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology. ECCV (62) 2024: 56-73- [i20]Yuxuan Sun, Yunlong Zhang, Yixuan Si, Chenglu Zhu, Zhongyi Shui, Kai Zhang, Jingxiong Li, Xingheng Lyu, Tao Lin, Lin Yang:
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration. CoRR abs/2407.00203 (2024) - 2023
- [i19]Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich:
Decentralized Gradient Tracking with Local Steps. CoRR abs/2301.01313 (2023) - 2022
- [b1]Tao Lin:
Algorithms for Efficient and Robust Distributed Deep Learning. EPFL, Switzerland, 2022 - [j4]Jie Su, Zhenyu Wen, Tao Lin, Yu Guan:
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6(1): 28:1-28:19 (2022) - [i18]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR abs/2202.03836 (2022) - [i17]Jie Su, Zhenyu Wen, Tao Lin, Yu Guan:
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition. CoRR abs/2202.07260 (2022) - 2021
- [c16]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. ICML 2021: 5686-5696 - [c15]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML 2021: 6654-6665 - [c14]Anastasia Koloskova, Tao Lin, Sebastian U. Stich:
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS 2021: 11422-11435 - [c13]Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. NeurIPS 2021: 28004-28015 - [i16]Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2102.04761 (2021) - [i15]Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich:
Consensus Control for Decentralized Deep Learning. CoRR abs/2102.04828 (2021) - [i14]Fei Mi, Tao Lin, Boi Faltings:
Representation Memorization for Fast Learning New Knowledge without Forgetting. CoRR abs/2108.12596 (2021) - [i13]Thijs Vogels, Lie He, Anastasia Koloskova, Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi:
RelaySum for Decentralized Deep Learning on Heterogeneous Data. CoRR abs/2110.04175 (2021) - [i12]Futong Liu, Tao Lin, Martin Jaggi:
Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation. CoRR abs/2112.08798 (2021) - 2020
- [j3]Zhenyu Wen, Tao Lin, Renyu Yang, Shouling Ji, Rajiv Ranjan, Alexander B. Romanovsky, Chang-Ting Lin, Jie Xu:
GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds. IEEE Trans. Parallel Distributed Syst. 31(1): 129-143 (2020) - [c12]Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, Boi Faltings:
Generalized Class Incremental Learning. CVPR Workshops 2020: 970-974 - [c11]Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze:
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. EMNLP (1) 2020: 2226-2241 - [c10]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR 2020 - [c9]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. ICLR 2020 - [c8]Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi:
Don't Use Large Mini-batches, Use Local SGD. ICLR 2020 - [c7]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. ICML 2020: 6094-6104 - [c6]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS 2020 - [c5]Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk:
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. NeurIPS 2020 - [i11]Mengjie Zhao, Tao Lin, Martin Jaggi, Hinrich Schütze:
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. CoRR abs/2004.12406 (2020) - [i10]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Extrapolation for Large-batch Training in Deep Learning. CoRR abs/2006.05720 (2020) - [i9]Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi:
Ensemble Distillation for Robust Model Fusion in Federated Learning. CoRR abs/2006.07242 (2020) - [i8]Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi:
Dynamic Model Pruning with Feedback. CoRR abs/2006.07253 (2020) - [i7]Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk:
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. CoRR abs/2006.08403 (2020)
2010 – 2019
- 2019
- [c4]Tian Guo, Tao Lin, Nino Antulov-Fantulin:
Exploring interpretable LSTM neural networks over multi-variable data. ICML 2019: 2494-2504 - [i6]Tian Guo, Tao Lin, Nino Antulov-Fantulin:
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data. CoRR abs/1905.12034 (2019) - [i5]Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi:
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR abs/1907.09356 (2019) - 2018
- [c3]Tian Guo, Tao Lin, Yao Lu:
An interpretable LSTM neural network for autoregressive exogenous model. ICLR (Workshop) 2018 - [c2]Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi:
Training DNNs with Hybrid Block Floating Point. NeurIPS 2018: 451-461 - [i4]Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi:
End-to-End DNN Training with Block Floating Point Arithmetic. CoRR abs/1804.01526 (2018) - [i3]Tian Guo, Tao Lin, Yao Lu:
An interpretable LSTM neural network for autoregressive exogenous model. CoRR abs/1804.05251 (2018) - [i2]Tian Guo, Tao Lin:
Multi-variable LSTM neural network for autoregressive exogenous model. CoRR abs/1806.06384 (2018) - [i1]Tao Lin, Sebastian U. Stich, Martin Jaggi:
Don't Use Large Mini-Batches, Use Local SGD. CoRR abs/1808.07217 (2018) - 2017
- [j2]Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, Michael Rovatsos:
Fog Orchestration for Internet of Things Services. IEEE Internet Comput. 21(2): 16-24 (2017) - [j1]Rachid Guerraoui, Anne-Marie Kermarrec, Tao Lin, Rhicheek Patra:
Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar. Proc. VLDB Endow. 10(10): 1070-1081 (2017) - [c1]Tao Lin, Tian Guo, Karl Aberer:
Hybrid Neural Networks for Learning the Trend in Time Series. IJCAI 2017: 2273-2279
Coauthor Index
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last updated on 2024-12-15 02:18 CET by the dblp team
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