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2020 – today
- 2023
[c9]Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun:
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models. ICML 2023: 16639-16653- 2022
- [c8]Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, Byung-Gon Chun:
Orca: A Distributed Serving System for Transformer-Based Generative Models. OSDI 2022: 521-538 - [i9]Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun:
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs. CoRR abs/2201.09210 (2022) - 2021
- [j2]Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi:
WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. Proc. VLDB Endow. 15(1): 11-20 (2021) - [c7]Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun:
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs. NeurIPS 2021: 1468-1480 - 2020
- [c6]Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun:
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. NeurIPS 2020 - [c5]Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi:
A Tensor Compiler for Unified Machine Learning Prediction Serving. OSDI 2020: 899-917 - [i8]Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Yunseong Lee, Byung-Gon Chun:
Accelerating Multi-Model Inference by Merging DNNs of Different Weights. CoRR abs/2009.13062 (2020) - [i7]Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi:
A Tensor Compiler for Unified Machine Learning Prediction Serving. CoRR abs/2010.04804 (2020) - [i6]Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun:
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. CoRR abs/2012.02732 (2020)
2010 – 2019
- 2019
- [j1]Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Taebum Kim, Byung-Gon Chun:
Speculative Symbolic Graph Execution of Imperative Deep Learning Programs. ACM SIGOPS Oper. Syst. Rev. 53(1): 26-33 (2019) - [c4]Soojeong Kim, Gyeong-In Yu, Hojin Park
, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun:
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks. EuroSys 2019: 43:1-43:15 - [c3]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:
Automating System Configuration of Distributed Machine Learning. ICDCS 2019: 2057-2067 - [c2]Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Byung-Gon Chun:
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. NSDI 2019: 453-468 - [i5]Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi:
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach. CoRR abs/1906.03822 (2019) - [i4]Ahnjae Shin, Dongjin Shin, Sungwoo Cho, Do Yoon Kim, Eunji Jeong, Gyeong-In Yu, Byung-Gon Chun:
Stage-based Hyper-parameter Optimization for Deep Learning. CoRR abs/1911.10504 (2019) - 2018
- [c1]Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun:
Improving the expressiveness of deep learning frameworks with recursion. EuroSys 2018: 19:1-19:13 - [i3]Soojeong Kim, Gyeong-In Yu, Hojin Park, Sungwoo Cho, Eunji Jeong, Hyeonmin Ha, Sanha Lee, Joo Seong Jeong, Byung-Gon Chun:
Parallax: Automatic Data-Parallel Training of Deep Neural Networks. CoRR abs/1808.02621 (2018) - [i2]Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun:
Improving the Expressiveness of Deep Learning Frameworks with Recursion. CoRR abs/1809.00832 (2018) - [i1]Eunji Jeong, Sungwoo Cho, Gyeong-In Yu, Joo Seong Jeong, Dongjin Shin, Byung-Gon Chun:
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs. CoRR abs/1812.01329 (2018)
Coauthor Index
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