6 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities
Muchang Bahng, Charlie Berens, Jon Donnelly +3
Species detection is important for monitoring the health of ecosystems and identifying invasive species, serving a crucial role in guiding conservation efforts. Multimodal neural n…
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
Jon Donnelly, Zhicheng Guo, Alina Jade Barnett +3
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part mode…
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…
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…
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
Chiyu Ma, Brandon Zhao, Chaofan Chen +1
We present ProtoConcepts, a method for interpretable image classification combining deep learning and case-based reasoning using prototypical parts. Existing work in prototype-base…