Improving the expressiveness of deep learning frameworks with recursion. - Eunji Jeong; Joo Seong Jeong; Soojeong Kim; Gyeong-In Yu; Byung-Gon Chun (EuroSys, 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)

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 (EuroSys, 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 (ICDCS, 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 (NSDI, 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)

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)

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)

Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning. - Woosuk Kwon; Gyeong-In Yu; Eunji Jeong; Byung-Gon Chun (NeurIPS, 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 (OSDI, 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)

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 (NeurIPS, 2021)

WindTunnel: Towards Differentiable ML Pipelines Beyond a Single Modele. - Gyeong-In Yu; Saeed Amizadeh; Sehoon Kim; Artidoro Pagnoni; Ce Zhang 001; Byung-Gon Chun; Markus Weimer; Matteo Interlandi (Proc. VLDB Endow., 2021)

Orca: A Distributed Serving System for Transformer-Based Generative Models. - Gyeong-In Yu; Joo Seong Jeong; Geon-Woo Kim; Soojeong Kim; Byung-Gon Chun (OSDI, 2022)

BPipe: Memory-Balanced Pipeline Parallelism for Training Large Language Models. - Taebum Kim; Hyoungjoo Kim; Gyeong-In Yu; Byung-Gon Chun (ICML, 2023)