Sunwoo Lee
Sunwoo Lee
Assistant Professor, Inha University
Verified email at - Homepage
Cited by
Cited by
Parallel deep convolutional neural network training by exploiting the overlapping of computation and communication
S Lee, D Jha, A Agrawal, A Choudhary, W Liao
2017 IEEE 24th international conference on high performance computing (HiPC …, 2017
Ssfl: Tackling label deficiency in federated learning via personalized self-supervision
C He, Z Yang, E Mushtaq, S Lee, M Soltanolkotabi, S Avestimehr
arXiv preprint arXiv:2110.02470, 2021
Improving scalability of parallel CNN training by adjusting mini-batch size at run-time
S Lee, Q Kang, S Madireddy, P Balaprakash, A Agrawal, A Choudhary, ...
2019 IEEE International Conference on Big Data (Big Data), 830-839, 2019
Layer-wise adaptive model aggregation for scalable federated learning
S Lee, T Zhang, AS Avestimehr
Proceedings of the AAAI Conference on Artificial Intelligence 37 (7), 8491-8499, 2023
Fedaudio: A federated learning benchmark for audio tasks
T Zhang, T Feng, S Alam, S Lee, M Zhang, SS Narayanan, S Avestimehr
ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and …, 2023
Gpt-fl: Generative pre-trained model-assisted federated learning
T Zhang, T Feng, S Alam, D Dimitriadis, M Zhang, SS Narayanan, ...
arXiv preprint arXiv:2306.02210, 2023
Improving all-to-many personalized communication in two-phase i/o
Q Kang, R Ross, R Latham, S Lee, A Agrawal, A Choudhary, W Liao
SC20: International Conference for High Performance Computing, Networking …, 2020
Improving mpi collective i/o for high volume non-contiguous requests with intra-node aggregation
Q Kang, S Lee, K Hou, R Ross, A Agrawal, A Choudhary, W Liao
IEEE Transactions on Parallel and Distributed Systems 31 (11), 2682-2695, 2020
TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training
T Zhang, L Gao, S Lee, M Zhang, S Avestimehr
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Federated learning of large models at the edge via principal sub-model training
Y Niu, S Prakash, S Kundu, S Lee, S Avestimehr
arXiv preprint arXiv:2208.13141, 2022
Communication-efficient parallelization strategy for deep convolutional neural network training
S Lee, A Agrawal, P Balaprakash, A Choudhary, WK Liao
2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC), 47-56, 2018
Evaluation of K-means data clustering algorithm on Intel Xeon Phi
S Lee, W Liao, A Agrawal, N Hardavellas, A Choudhary
2016 IEEE International Conference on Big Data (Big Data), 2251-2260, 2016
Parallel community detection algorithm using a data partitioning strategy with pairwise subdomain duplication
D Palsetia, W Hendrix, S Lee, A Agrawal, W Liao, A Choudhary
High Performance Computing: 31st International Conference, ISC High …, 2016
Partial model averaging in federated learning: Performance guarantees and benefits
S Lee, AK Sahu, C He, S Avestimehr
Neurocomputing 556, 126647, 2023
A case study on parallel HDF5 dataset concatenation for high energy physics data analysis
S Lee, K Hou, K Wang, S Sehrish, M Paterno, J Kowalkowski, Q Koziol, ...
Parallel Computing 110, 102877, 2022
Probing oxygen vacancy distribution in oxide heterostructures by deep Learning-based spectral analysis of current noise
S Lee, J Jeon, H Lee
Applied Surface Science 604, 154599, 2022
FedML Parrot: A scalable federated learning system via heterogeneity-aware scheduling on sequential and hierarchical training
Z Tang, X Chu, RY Ran, S Lee, S Shi, Y Zhang, Y Wang, AQ Liang, ...
arXiv preprint arXiv:2303.01778, 2023
Variance-aware weight quantization of multi-level resistive switching devices based on Pt/LaAlO3/SrTiO3 heterostructures
S Lee, J Jeon, K Eom, C Jeong, Y Yang, JY Park, CB Eom, H Lee
Scientific Reports 12 (1), 9068, 2022
In situ compression artifact removal in scientific data using deep transfer learning and experience replay
S Madireddy, JH Park, S Lee, P Balaprakash, S Yoo, W Liao, CD Hauck, ...
Machine Learning: Science and Technology 2 (2), 025010, 2020
Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning
S Lee, Q Kang, A Agrawal, A Choudhary, W Liao
2020 IEEE International Conference on Big Data (Big Data), 718-727, 2020
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