Nakul Verma (Janelia Farm Research Campus, HHMI)
A tutorial on metric learning with some recent advances.
Goal of metric learning is to learn a notion of distance in the representation space that yields good prediction performance on data. In this tutorial we explore some classic ways one can efficiently find good metrics. Starting from the basics, we'll cover classic techniques like Large Margin Nearest Neighbor (LMNN) and Information Theoretic Metric Learning (ITML) and discuss key principles what makes these techniques effective. We will also study some extensions and see how metric learning has helped in ranking problems (information retrieval) and large scale classification.
Dr. Nakul Verma is a research specialist at Janelia Farm Research Campus, a center for conducting fundamental research in basic sciences, where he is developing novel statistical techniques to help biologists quantitatively analyze behavioral phenotypes in model organisms and better understand the underlying neuroscience and genetic principles. His interests include high dimensional data analysis and exploiting intrinsic structure in data to design effective learning algorithms. Previously Dr. Verma worked at Amazon as a research scientist developing risk assessment models for real-time fraud detection. Dr. Verma received his PhD in Computer Science from UC San Diego specializing in Machine Learning.
-Flurry for hosting
-Tommy Chheng for recording
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