Tao Lin

According to our database, Tao Lin authored at least 28 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

Decentralized Gradient Tracking with Local Steps.

Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich

CoRR, 2023

2022

Algorithms for Efficient and Robust Distributed Deep Learning.

Tao Lin

PhD thesis, 2022

Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition.

Jie Su, Zhenyu Wen, Tao Lin, Yu Guan

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 2022

2021

Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation.

Futong Liu, Tao Lin, Martin Jaggi

CoRR, 2021

Representation Memorization for Fast Learning New Knowledge without Forgetting.

Fei Mi, Tao Lin, Boi Faltings

CoRR, 2021

RelaySum for Decentralized Deep Learning on Heterogeneous Data.

Thijs Vogels, Lie He, Anastasia Koloskova, Sai Praneeth Karimireddy, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Improved Analysis of Gradient Tracking for Decentralized Machine Learning.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.

Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi

Proceedings of the 38th International Conference on Machine Learning, 2021

Consensus Control for Decentralized Deep Learning.

Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

GA-Par: Dependable Microservice Orchestration Framework for Geo-Distributed Clouds.

Zhenyu Wen, Tao Lin, Renyu Yang, Shouling Ji, Rajiv Ranjan, Alexander B. Romanovsky, Chang-Ting Lin, Jie Xu

IEEE Trans. Parallel Distributed Syst., 2020

Masking as an Efficient Alternative to Finetuning for Pretrained Language Models.

Mengjie Zhao, Tao Lin, Martin Jaggi, Hinrich Schütze

CoRR, 2020

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them.

Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, Sabine Süsstrunk

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Ensemble Distillation for Robust Model Fusion in Federated Learning.

Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Extrapolation for Large-batch Training in Deep Learning.

Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi

Proceedings of the 37th International Conference on Machine Learning, 2020

Don't Use Large Mini-batches, Use Local SGD.

Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Dynamic Model Pruning with Feedback.

Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Decentralized Deep Learning with Arbitrary Communication Compression.

Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi

Proceedings of the 8th International Conference on Learning Representations, 2020

Masking as an Efficient Alternative to Finetuning for Pretrained Language Models.

Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

Generalized Class Incremental Learning.

Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, Boi Faltings

Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019

Exploring interpretable LSTM neural networks over multi-variable data.

Tian Guo, Tao Lin, Nino Antulov-Fantulin

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Don't Use Large Mini-Batches, Use Local SGD.

Tao Lin, Sebastian U. Stich, Martin Jaggi

CoRR, 2018

Multi-variable LSTM neural network for autoregressive exogenous model.

Tian Guo, Tao Lin

CoRR, 2018

End-to-End DNN Training with Block Floating Point Arithmetic.

Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi

CoRR, 2018

Training DNNs with Hybrid Block Floating Point.

Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

An interpretable LSTM neural network for autoregressive exogenous model.

Tian Guo, Tao Lin, Yao Lu

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar.

Rachid Guerraoui, Anne-Marie Kermarrec, Tao Lin, Rhicheek Patra

Proc. VLDB Endow., 2017

Fog Orchestration for Internet of Things Services.

Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, Michael Rovatsos

IEEE Internet Comput., 2017

Hybrid Neural Networks for Learning the Trend in Time Series.

Tao Lin, Tian Guo, Karl Aberer

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017