170 citations · 205 across the 6 of their papers we have counts for
4 papers · 1 filter
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
Taebum Kim, Eunji Jeong, Geon-Woo Kim +4
Imperative programming allows users to implement their deep neural networks (DNNs) easily and has become an essential part of recent deep learning (DL) frameworks. Recently, severa…
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
Woosuk Kwon, Gyeong-In Yu, Eunji Jeong +1
Deep learning (DL) frameworks take advantage of GPUs to improve the speed of DL inference and training. Ideally, DL frameworks should be able to fully utilize the computation power…
Stage-based Hyper-parameter Optimization for Deep Learning
Ahnjae Shin, Dong-Jin Shin, Sungwoo Cho +4
As deep learning techniques advance more than ever, hyper-parameter optimization is the new major workload in deep learning clusters. Although hyper-parameter optimization is cruci…
Improving the Expressiveness of Deep Learning Frameworks with Recursion
Eunji Jeong, Joo Seong Jeong, Soojeong Kim +2
Recursive neural networks have widely been used by researchers to handle applications with recursively or hierarchically structured data. However, embedded control flow deep learni…