Gyeong-In Yu

Publication Activity (10 Years)

Years Active: 2018-2023
Publications (10 Years): 20

Top Topics

Parameter Optimization

Learning Frameworks

Top Venues

Publications

Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun
BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models. ICML (2023)
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)
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 (2022)
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) (2021)
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)
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 (2020)
Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Yunseong Lee, Byung-Gon Chun
Accelerating Multi-Model Inference by Merging DNNs of Different Weights. CoRR (2020)
Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. CoRR (2020)
Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. NeurIPS (2020)
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)
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)
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)
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)
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 (2019)
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 (2019)
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) (2019)
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 (2018)
Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun
Improving the expressiveness of deep learning frameworks with recursion. EuroSys (2018)
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 (2018)
Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun
Improving the Expressiveness of Deep Learning Frameworks with Recursion. CoRR (2018)