Prof. Cynthia Rudin (Duke University).
Two Indispensable Tools for Scientific Discovery
I will talk about two types of tools that our lab finds invaluable for scientific discovery – dimension reduction algorithms and interpretable prototype-based neural networks.
Dimension reduction algorithms provide unique insight into the structure of high-dimensional data. They are ubiquitous in the biological sciences. One tension that has always faced these methods is the apparent trade-off between preservation of global structure and preservation of local structure. In recent work, we have found that the mechanisms controlling these are separate, which means we can control both local and global structure simultaneously. These insights led to the powerful PaCMAP algorithm, which has now 600+ github stars and has won two best software awards from the American Statistical Association. I will discuss also how we are building on PaCMAP in recent work on dimension reduction for data visualization, specifically the recently published LocalMAP, ParamPaCMAP and ParamRepulsor algorithms. I will discuss applications to a wide variety of applications in genomics, name-ethnicity analysis, finance, and ICU neurology.
Prototype neural networks are arguably the most popular type of inherently interpretable neural networks for computer vision and signal processing. These algorithms make predictions by comparing parts of an image to parts of prototypical images, where each comparison receives a score, and the scores are summed to form the final class prediction. I will discuss the popular ProtoPNet algorithm, as well as its extension to ProtoConcept, where a cluster of images becomes a “concept prototype.” This makes comparisons more informative. I will briefly mention ProtoViT, a visual transformer adaptation of ProtoPNet. I will discuss an application to ICU neurology.
Here are two of the papers I will discuss:
Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Journal of Machine Learning Research (JMLR), 2021
https://jmlr.org/papers/v22/20-1061.html
Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, Cynthia Rudin
This Looks Like That: Deep Learning for Interpretable Image Recognition.
NeurIPS spotlight, 2019.
https://arxiv.org/abs/1806.10574
