47 citations · 74 across the 6 of their papers we have counts for
3 papers · 1 filter
Stochastic Gradient Descent without Full Data Shuffle
Lijie Xu, Shuang Qiu, Binhang Yuan +9
Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherent…
SHiFT: An Efficient, Flexible Search Engine for Transfer Learning
Cedric Renggli, Xiaozhe Yao, Luka Kolar +3
Transfer learning can be seen as a data- and compute-efficient alternative to training models from scratch. The emergence of rich model repositories, such as TensorFlow Hub, enable…
Experiments as Code: A Concept for Reproducible, Auditable, Debuggable, Reusable, & Scalable Experiments
Leonel Aguilar, Michal Gath-Morad, Jascha Grübel +7
A common concern in experimental research is the auditability and reproducibility of experiments. Experiments are usually designed, provisioned, managed, and analyzed by diverse te…