Gyeong-In Yu

According to our database, Gyeong-In Yu authored at least 15 papers between 2018 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models.

Taebum Kim, Hyoungjoo Kim, Gyeong-In Yu, Byung-Gon Chun

Proceedings of the International Conference on Machine Learning, 2023

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

2021

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele.

Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim, Artidoro Pagnoni, Ce Zhang, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

Proc. VLDB Endow., 2021

Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs.

Taebum Kim, Eunji Jeong, Geon-Woo Kim, Yunmo Koo, Sehoon Kim, Gyeong-In Yu, Byung-Gon Chun

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

A Tensor Compiler for Unified Machine Learning Prediction Serving.

Supun Nakandala, Karla Saur, Gyeong-In Yu, Konstantinos Karanasos, Carlo Curino, Markus Weimer, Matteo Interlandi

Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation, 2020

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning.

Woosuk Kwon, Gyeong-In Yu, Eunji Jeong, Byung-Gon Chun

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Stage-based Hyper-parameter Optimization for Deep Learning.

Ahnjae Shin, Dongjin Shin, Sungwoo Cho, Do Yoon Kim, Eunji Jeong, Gyeong-In Yu, Byung-Gon Chun

CoRR, 2019

Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach.

Gyeong-In Yu, Saeed Amizadeh, Artidoro Pagnoni, Byung-Gon Chun, Markus Weimer, Matteo Interlandi

CoRR, 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