47 citations · 141 across the 11 of their papers we have counts for
11 papers
Hydra: A System for Large Multi-Model Deep Learning
Kabir Nagrecha, Arun Kumar
Scaling up model depth and size is now a common approach to raise accuracy in many deep learning (DL) applications, as evidenced by the widespread success of multi-billion or even…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Model-based Pricing for Machine Learning in a Data Marketplace
Lingjiao Chen, Paraschos Koutris, Arun Kumar
Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the t…
In-RDBMS Hardware Acceleration of Advanced Analytics
Divya Mahajan, Joon Kyung Kim, Jacob Sacks +3
The data revolution is fueled by advances in machine learning, databases, and hardware design. Programmable accelerators are making their way into each of these areas independently…
Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers?
Vraj Shah, Arun Kumar, Xiaojin Zhu
Machine learning (ML) over relational data is a booming area of the database industry and academia. While several projects aim to build scalable and fast ML systems, little work ha…
Tuple-oriented Compression for Large-scale Mini-batch Stochastic Gradient Descent
Fengan Li, Lingjiao Chen, Yijing Zeng +4
Data compression is a popular technique for improving the efficiency of data processing workloads such as SQL queries and more recently, machine learning (ML) with classical batch…