Sunwoo Lee | DeepAI

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∙ 08/22/2023

FoX: Formation-aware exploration in multi-agent reinforcement learning

Recently, deep multi-agent reinforcement learning (MARL) has gained sign...

0 Yonghyeon Jo, et al. ∙

∙ 07/25/2023

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

Quasi-Newton methods still face significant challenges in training large...

0 Yue Niu, et al. ∙

∙ 03/03/2023

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

Federated Learning (FL) enables collaborations among clients for train m...

2 Zhenheng Tang, et al. ∙

∙ 05/02/2022

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

In High Energy Physics (HEP), experimentalists generate large volumes of...

2 Sunwoo Lee, et al. ∙

∙ 01/11/2022

Partial Model Averaging in Federated Learning: Performance Guarantees and Benefits

Local Stochastic Gradient Descent (SGD) with periodic model averaging (F...

0 Sunwoo Lee, et al. ∙

∙ 10/19/2021

Layer-wise Adaptive Model Aggregation for Scalable Federated Learning

In Federated Learning, a common approach for aggregating local models ac...

16 Sunwoo Lee, et al. ∙

∙ 10/06/2021

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

Federated Learning (FL) is transforming the ML training ecosystem from a...

6 Chaoyang He, et al. ∙

∙ 07/29/2019

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

Two-phase I/O is a well-known strategy for implementing collective MPI-I...

0 Qiao Kang, et al. ∙

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