Out-of-Distribution Detection
About this resource
The below tutorial from Sharon Li, an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison, introduces a pervasive problem faced by many machine learning systems deployed in the wild — out-of-distribution data.
Out-of-distribution data, often overlooked but immensely consequential, poses a significant threat to the reliability and efficacy of machine learning models. Through Sharon’s presentation, viewers gain a comprehensive understanding of this complex phenomenon and its potential ramifications on predictive accuracy.
Check out the video below to learn more about this problem and the cutting-edge methods you can equip yourself with to prevent inaccurate model predictions.
Coming Soon…
Check this page again soon for worked examples and exercises (code provided)!
Questions?
If you any lingering questions about this resource, please feel free to post to the Nexus Q&A on GitHub. We will improve materials on this website as additional questions come in.