2 papers
cs.DB2023
Scardina: Scalable Join Cardinality Estimation by Multiple Density Estimators
Ryuichi Ito, Yuya Sasaki, Chuan Xiao +1
In recent years, machine learning-based cardinality estimation methods are replacing traditional methods. This change is expected to contribute to one of the most important applica…
cs.LG2022
Scaling Private Deep Learning with Low-Rank and Sparse Gradients
Ryuichi Ito, Seng Pei Liew, Tsubasa Takahashi +2
Applying Differentially Private Stochastic Gradient Descent (DPSGD) to training modern, large-scale neural networks such as transformer-based models is a challenging task, as the m…