3 papers
cs.CV2026
Aristotelian Manifolds: Leveraging Platonic Perceptual Features for Backpropagation Free Rapid Concept Learning
Michael Karnes, Alper Yilmaz
This paper formalizes and systematically characterizes Aristotelian Manifolds, a generalized structural framework built upon the Platonic Representation Hypothesis. We position hig…
cs.CV2026
Rethinking the Good Enough Embedding for Easy Few-Shot Learning
Michael Karnes, Alper Yilmaz
The field of deep visual recognition is undergoing a paradigm shift toward universal representations. The Platonic Representation Hypothesis suggests that diverse architectures tra…
cs.CV2026
Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST
Michael Karnes, Alper Yilmaz
While deep learning has achieved remarkable success in medical imaging, the "black-box" nature of backpropagation-based models remains a significant barrier to clinical adoption. T…