171 citations · 244 across the 10 of their papers we have counts for
5 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…
Accelerating Approximate Aggregation Queries with Expensive Predicates
Daniel Kang, John Guibas, Peter Bailis +3
Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using e…
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…
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…