15 citations · 33 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
Accelerating Multi-Model Inference by Merging DNNs of Different Weights
Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu +2
Standardized DNN models that have been proved to perform well on machine learning tasks are widely used and often adopted as-is to solve downstream tasks, forming the transfer lear…
Hippo: Taming Hyper-parameter Optimization of Deep Learning with Stage Trees
Ahnjae Shin, Do Yoon Kim, Joo Seong Jeong +1
Hyper-parameter optimization is crucial for pushing the accuracy of a deep learning model to its limits. A hyper-parameter optimization job, referred to as a study, involves numero…
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…
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
Gyeong-In Yu, Saeed Amizadeh, Sehoon Kim +4
Classical Machine Learning (ML) pipelines often comprise of multiple ML models where models, within a pipeline, are trained in isolation. Conversely, when training neural network m…
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Yunseong Lee, Alberto Scolari, Byung-Gon Chun +3
Machine Learning models are often composed of pipelines of transformations. While this design allows to efficiently execute single model components at training time, prediction ser…