5 papers
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…
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…
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…
Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects
Michael Karnes, Alper Yilmaz
Feature extraction has always been a critical component of the computer vision field. More recently, state-of-the-art computer visions algorithms have incorporated Deep Neural Netw…
Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System
Michael Karnes, Shehan Perera, Srikar Adhikari +1
This paper presents a novel ultrasound imaging point-of-care (PoC) COVID-19 diagnostic system. The adaptive visual diagnostics utilize few-shot learning (FSL) to generate encoded d…