Tao Lin

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 39

Top Topics

Weakly Supervised

Top Venues

Publications

Yue Liu, Tao Lin, Anastasia Koloskova, Sebastian U. Stich
Decentralized Gradient Tracking with Local Steps. CoRR (2023)
Jie Su, Zhenyu Wen, Tao Lin, Yu Guan
Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition. CoRR (2022)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. CoRR (2022)
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) (2022)
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
Quasi-global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. ICML (2021)
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 (2021)
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)
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
Consensus Control for Decentralized Deep Learning. CoRR (2021)
Lingjing Kong, Tao Lin, Anastasia Koloskova, Martin Jaggi, Sebastian U. Stich
Consensus Control for Decentralized Deep Learning. ICML (2021)
Fei Mi, Tao Lin, Boi Faltings
Representation Memorization for Fast Learning New Knowledge without Forgetting. CoRR (2021)
Tao Lin, Sai Praneeth Karimireddy, Sebastian U. Stich, Martin Jaggi
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data. CoRR (2021)
Futong Liu, Tao Lin, Martin Jaggi
Understanding Memorization from the Perspective of Optimization via Efficient Influence Estimation. CoRR (2021)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning. NeurIPS (2021)
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
Extrapolation for Large-batch Training in Deep Learning. ICML (2020)
Tao Lin, Sebastian U. Stich, Kumar Kshitij Patel, Martin Jaggi
Don't Use Large Mini-batches, Use Local SGD. ICLR (2020)
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 (2020)
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
Ensemble Distillation for Robust Model Fusion in Federated Learning. NeurIPS (2020)
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
Ensemble Distillation for Robust Model Fusion in Federated Learning. CoRR (2020)
Tao Lin, Lingjing Kong, Sebastian U. Stich, Martin Jaggi
Extrapolation for Large-batch Training in Deep Learning. CoRR (2020)
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)
Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, Boi Faltings
Generalized Class Incremental Learning. CVPR Workshops (2020)
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi
Dynamic Model Pruning with Feedback. CoRR (2020)
Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, Hinrich Schütze
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. EMNLP (1) (2020)
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) (2020)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
Decentralized Deep Learning with Arbitrary Communication Compression. ICLR (2020)
Mengjie Zhao, Tao Lin, Martin Jaggi, Hinrich Schütze
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models. CoRR (2020)
Tao Lin, Sebastian U. Stich, Luis Barba, Daniil Dmitriev, Martin Jaggi
Dynamic Model Pruning with Feedback. ICLR (2020)
Anastasia Koloskova, Tao Lin, Sebastian U. Stich, Martin Jaggi
Decentralized Deep Learning with Arbitrary Communication Compression. CoRR (2019)
Tian Guo, Tao Lin, Nino Antulov-Fantulin
Exploring interpretable LSTM neural networks over multi-variable data. ICML (2019)
Tian Guo, Tao Lin, Nino Antulov-Fantulin
Exploring Interpretable LSTM Neural Networks over Multi-Variable Data. CoRR (2019)
Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi
Training DNNs with Hybrid Block Floating Point. NeurIPS (2018)
Tian Guo, Tao Lin
Multi-variable LSTM neural network for autoregressive exogenous model. CoRR (2018)
Mario Drumond, Tao Lin, Martin Jaggi, Babak Falsafi
End-to-End DNN Training with Block Floating Point Arithmetic. CoRR (2018)
Tao Lin, Sebastian U. Stich, Martin Jaggi
Don't Use Large Mini-Batches, Use Local SGD. CoRR (2018)
Tian Guo, Tao Lin, Yao Lu
An interpretable LSTM neural network for autoregressive exogenous model. ICLR (Workshop) (2018)
Tian Guo, Tao Lin, Yao Lu
An interpretable LSTM neural network for autoregressive exogenous model. CoRR (2018)
Tao Lin, Tian Guo, Karl Aberer
Hybrid Neural Networks for Learning the Trend in Time Series. IJCAI (2017)
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) (2017)
Zhenyu Wen, Renyu Yang, Peter Garraghan, Tao Lin, Jie Xu, Michael Rovatsos
Fog Orchestration for Internet of Things Services. IEEE Internet Comput. 21 (2) (2017)