6 citations · 16 across the 12 of their papers we have counts for
5 papers · 1 filter
Interpretable Generalized Additive Models for Datasets with Missing Values
Hayden McTavish, Jon Donnelly, Margo Seltzer +1
Many important datasets contain samples that are missing one or more feature values. Maintaining the interpretability of machine learning models in the presence of such missing dat…
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
Chiyu Ma, Jon Donnelly, Wenjun Liu +3
We present ProtoViT, a method for interpretable image classification combining deep learning and case-based reasoning. This method classifies an image by comparing it to a set of l…
Amazing Things Come From Having Many Good Models
Cynthia Rudin, Chudi Zhong, Lesia Semenova +7
The Rashomon Effect, coined by Leo Breiman, describes the phenomenon that there exist many equally good predictive models for the same dataset. This phenomenon happens for many rea…
This Looks Better than That: Better Interpretable Models with ProtoPNeXt
Frank Willard, Luke Moffett, Emmanuel Mokel +6
Prototypical-part models are a popular interpretable alternative to black-box deep learning models for computer vision. However, they are difficult to train, with high sensitivity…
FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography
Julia Yang, Alina Jade Barnett, Jon Donnelly +6
Digital mammography is essential to breast cancer detection, and deep learning offers promising tools for faster and more accurate mammogram analysis. In radiology and other high-s…