4 citations · 4 across the 4 of their papers we have counts for
4 papers
SneakPeek: Data-Aware Model Selection and Scheduling for Inference Serving on the Edge
Joel Wolfrath, Daniel Frink, Abhishek Chandra
Modern applications increasingly rely on inference serving systems to provide low-latency insights with a diverse set of machine learning models. Existing systems often utilize res…
Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
Nathan Wolfrath, Joel Wolfrath, Hengrui Hu +2
Machine Learning (ML) research has increased substantially in recent years, due to the success of predictive modeling across diverse application domains. However, well-known barrie…
A Biased Estimator for MinMax Sampling and Distributed Aggregation
Joel Wolfrath, Abhishek Chandra
MinMax sampling is a technique for downsampling a real-valued vector which minimizes the maximum variance over all vector components. This approach is useful for reducing the amoun…
Efficient Transmission and Reconstruction of Dependent Data Streams via Edge Sampling
Joel Wolfrath, Abhishek Chandra
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for real-time analytics. Geo-distributed streaming systems, where cl…