Paper accepted at ACM Computing Surveys!

Our paper titled “The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey” has just been accepted for publication at ACM Computing Systems. In the paper, we draw parallels between distributed graph processing, deep learning and graph neural network systems. It brings together our differentContinue reading “Paper accepted at ACM Computing Surveys!”

SIGMOD Paper Accepted!

Our paper titled “Where is My Training Bottleneck? Hidden Trade-Offs in Deep Learning Preprocessing Pipelines” has been accepted for publication at the SIGMOD 2022 conference. In this paper, we provide an in-depth analysis of data preprocessing pipelines from four different machine learning domains. We introduce a new perspective on efficiently preparing datasets for end-to-end deepContinue reading “SIGMOD Paper Accepted!”