53 citations · 133 across the 6 of their papers we have counts for
6 papers
Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem
Yuki Asada, Victor Fu, Apurva Gandhi +8
We demonstrate Tensor Query Processor (TQP): a query processor that automatically compiles relational operators into tensor programs. By leveraging tensor runtimes such as PyTorch,…
End-to-end Optimization of Machine Learning Prediction Queries
Kwanghyun Park, Karla Saur, Dalitso Banda +3
Prediction queries are widely used across industries to perform advanced analytics and draw insights from data. They include a data processing part (e.g., for joining, filtering, c…
Query Processing on Tensor Computation Runtimes
Dong He, Supun Nakandala, Dalitso Banda +7
The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the numbe…
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
Amit Gupte, Alexey Romanov, Sahitya Mantravadi +6
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect. Ev…
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
Olga Poppe, Tayo Amuneke, Dalitso Banda +23
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Dat…
Large-Scale Intelligent Microservices
Mark Hamilton, Nick Gonsalves, Christina Lee +8
Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologie…