Joo Seong Jeong

According to our database, Joo Seong Jeong authored at least 13 papers between 2015 and 2022.

Collaborative distances:

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

2022

Hippo: Sharing Computations in Hyper-Parameter Optimization.

Ahnjae Shin, Joo Seong Jeong, Do Yoon Kim, Soyoung Jung, Byung-Gon Chun

Proc. VLDB Endow., 2022

Orca: A Distributed Serving System for Transformer-Based Generative Models.

Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, Byung-Gon Chun

Proceedings of the 16th USENIX Symposium on Operating Systems Design and Implementation, 2022

Band: coordinated multi-DNN inference on heterogeneous mobile processors.

Joo Seong Jeong, Jingyu Lee, Donghyun Kim, Changmin Jeon, Changjin Jeong, Youngki Lee, Byung-Gon Chun

Proceedings of the MobiSys '22: The 20th Annual International Conference on Mobile Systems, Applications and Services, Portland, Oregon, 27 June 2022, 2022

2020

Accelerating Multi-Model Inference by Merging DNNs of Different Weights.

Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Yunseong Lee, Byung-Gon Chun

CoRR, 2020

Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees.

Ahnjae Shin, Do Yoon Kim, Joo Seong Jeong, Byung-Gon Chun

CoRR, 2020

2019

Speculative Symbolic Graph Execution of Imperative Deep Learning Programs.

Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Taebum Kim, Byung-Gon Chun

ACM SIGOPS Oper. Syst. Rev., 2019

JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs.

Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Byung-Gon Chun

Proceedings of the 16th USENIX Symposium on Networked Systems Design and Implementation, 2019

Automating System Configuration of Distributed Machine Learning.

Woo-Yeon Lee, Markus Weimer, Brian Cho, Byung-Gon Chun, Yunseong Lee, Joo Seong Jeong, Gyeong-In Yu, Jooyeon Kim, Hojin Park, Beomyeol Jeon, Won Wook Song, Gunhee Kim

Proceedings of the 39th IEEE International Conference on Distributed Computing Systems, 2019

Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks.

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

Proceedings of the Fourteenth EuroSys Conference 2019, Dresden, Germany, March 25-28, 2019, 2019

2018

Parallax: Automatic Data-Parallel Training of Deep Neural Networks.

Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun

CoRR, 2018

Improving the expressiveness of deep learning frameworks with recursion.

Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun

Proceedings of the Thirteenth EuroSys Conference, 2018

2017

Apache REEF: Retainable Evaluator Execution Framework.

Byung-Gon Chun, Tyson Condie, Yingda Chen, Brian Cho, Andrew Chung, Carlo Curino, Chris Douglas, Matteo Interlandi, Beomyeol Jeon, Joo Seong Jeong, Gyewon Lee, Yunseong Lee, Tony Majestro, Dahlia Malkhi, Sergiy Matusevych, Brandon Myers, Mariia Mykhailova, Shravan M. Narayanamurthy, Joseph Noor, Raghu Ramakrishnan, Sriram Rao, Russell Sears, Beysim Sezgin, Taegeon Um, Julia Wang, Markus Weimer, Youngseok Yang

ACM Trans. Comput. Syst., 2017

2015

Elastic Memory: Bring Elasticity Back to In-Memory Big Data Analytics.

Joo Seong Jeong, Woo-Yeon Lee, Yunseong Lee, Youngseok Yang, Brian Cho, Byung-Gon Chun

Proceedings of the 15th Workshop on Hot Topics in Operating Systems, 2015