Sunwoo Lee

Orcid: 0000-0001-6334-3068

Affiliations:

Inha University, Incheon, Korea

According to our database¹, Sunwoo Lee authored at least 31 papers between 2009 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

2010

2012

2014

2016

2018

2020

2022

2024

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training.

[BibT_eX]

[DOI]

IEEE Trans. Mob. Comput., December, 2024

Layer-Wise Adaptive Gradient Norm Penalizing Method for Efficient and Accurate Deep Learning.

[BibT_eX]

[DOI]

Sunwoo Lee

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

2023

Partial model averaging in Federated Learning: Performance guarantees and benefits.

[BibT_eX]

[DOI]

Neurocomputing, November, 2023

Achieving small-batch accuracy with large-batch scalability via Hessian-aware learning rate adjustment.

[BibT_eX]

[DOI]

Sunwoo Lee

Chaoyang He

Salman Avestimehr

Neural Networks, January, 2023

Overcoming Resource Constraints in Federated Learning: Large Models Can Be Trained with only Weak Clients.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

mL-BFGS: A Momentum-based L-BFGS for Distributed Large-scale Neural Network Optimization.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training.

[BibT_eX]

[DOI]

CoRR, 2023

FedAudio: A Federated Learning Benchmark for Audio Tasks.

[BibT_eX]

[DOI]

Shrikanth S. Narayanan

Salman Avestimehr

Proceedings of the IEEE International Conference on Acoustics, 2023

TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training.

[BibT_eX]

[DOI]

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Layer-Wise Adaptive Model Aggregation for Scalable Federated Learning.

[BibT_eX]

[DOI]

Sunwoo Lee

Tuo Zhang

Amir Salman Avestimehr

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

A case study on parallel HDF5 dataset concatenation for high energy physics data analysis.

[BibT_eX]

[DOI]

Parallel Comput., 2022

Improving scalability of parallel CNN training by adaptively adjusting parameter update frequency.

[BibT_eX]

[DOI]

J. Parallel Distributed Comput., 2022

Federated Learning of Large Models at the Edge via Principal Sub-Model Training.

[BibT_eX]

[DOI]

CoRR, 2022

A Case Study on Parallel HDF5 Dataset Concatenation for High Energy Physics Data Analysis.

[BibT_eX]

[DOI]

CoRR, 2022

Using Multi-Resolution Data to Accelerate Neural Network Training in Scientific Applications.

[BibT_eX]

[DOI]

Proceedings of the 22nd IEEE International Symposium on Cluster, 2022

2021

In situ compression artifact removal in scientific data using deep transfer learning and experience replay.

[BibT_eX]

[DOI]

Mach. Learn. Sci. Technol., 2021

Layer-wise Adaptive Model Aggregation for Scalable Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2021

SSFL: Tackling Label Deficiency in Federated Learning via Personalized Self-Supervision.

[BibT_eX]

[DOI]

CoRR, 2021

SIGRNN: Synthetic Minority Instances Generation in Imbalanced Datasets using a Recurrent Neural Network.

[BibT_eX]

[DOI]

Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, 2021

Asynchronous I/O Strategy for Large-Scale Deep Learning Applications.

[BibT_eX]

[DOI]

Proceedings of the 28th IEEE International Conference on High Performance Computing, 2021

Supporting Data Compression in PnetCDF.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Improving MPI Collective I/O for High Volume Non-Contiguous Requests With Intra-Node Aggregation.

[BibT_eX]

[DOI]

IEEE Trans. Parallel Distributed Syst., 2020

Improving all-to-many personalized communication in two-phase I/O.

[BibT_eX]

[DOI]

Proceedings of the International Conference for High Performance Computing, 2020

Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE/ACM International Symposium on Cluster, 2020

Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning.

[BibT_eX]

[DOI]

Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019

Improving MPI Collective I/O Performance With Intra-node Request Aggregation.

[BibT_eX]

[DOI]

CoRR, 2019

Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2017

Parallel Deep Convolutional Neural Network Training by Exploiting the Overlapping of Computation and Communication.

[BibT_eX]

[DOI]

Proceedings of the 24th IEEE International Conference on High Performance Computing, 2017

2016

Parallel Community Detection Algorithm Using a Data Partitioning Strategy with Pairwise Subdomain Duplication.

[BibT_eX]

[DOI]

Proceedings of the High Performance Computing - 31st International Conference, 2016

Evaluation of K-means data clustering algorithm on Intel Xeon Phi.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2009

Extending Component-Based Approaches for Multithreaded Design of Multiprocessor Embedded Software.

[BibT_eX]

[DOI]

Proceedings of the 2009 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing, 2009

Sunwoo Lee

Timeline

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