171 citations · 234 across the 11 of their papers we have counts for
8 papers · 1 filter
Finding Label and Model Errors in Perception Data With Learned Observation Assertions
Daniel Kang, Nikos Arechiga, Sudeep Pillai +2
ML is being deployed in complex, real-world scenarios where errors have impactful consequences. In these systems, thorough testing of the ML pipelines is critical. A key component…
Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Kexin Rong, Yao Lu, Peter Bailis +2
Many big-data clusters store data in large partitions that support access at a coarse, partition-level granularity. As a result, approximate query processing via row-level sampling…
Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics
Daniel Kang, Ankit Mathur, Teja Veeramacheneni +2
While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researc…
Storyboard: Optimizing Precomputed Summaries for Aggregation
Edward Gan, Peter Bailis, Moses Charikar
An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggre…
Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference
Peter Kraft, Daniel Kang, Deepak Narayanan +3
Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss cr…
BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics
Daniel Kang, Peter Bailis, Matei Zaharia
Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of…