30 citations · 57 across the 12 of their papers we have counts for
16 papers
On the Status of Foundation Models for SAR Imagery
Nathan Inkawhich
In this work we investigate the viability of foundational AI/ML models for Synthetic Aperture Radar (SAR) object recognition tasks. We are inspired by the tremendous progress being…
Multi-layer Radial Basis Function Networks for Out-of-distribution Detection
Amol Khanna, Chenyi Ling, Derek Everett +2
Existing methods for out-of-distribution (OOD) detection use various techniques to produce a score, separate from classification, that determines how ``OOD'' an input is. Our insig…
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
Matthew Inkawhich, Nathan Inkawhich, Hao Yang +3
An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work i…
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data
Amol Khanna, Edward Raff, Nathan Inkawhich
Linear models are ubiquitous in data science, but are particularly prone to overfitting and data memorization in high dimensions. To guarantee the privacy of training data, differe…
Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble
Chenhui Xu, Fuxun Yu, Zirui Xu +2
Recent research underscores the pivotal role of the Out-of-Distribution (OOD) feature representation field scale in determining the efficacy of models in OOD detection. Consequentl…
Comprehensive OOD Detection Improvements
Anish Lakkapragada, Amol Khanna, Edward Raff +1
As machine learning becomes increasingly prevalent in impactful decisions, recognizing when inference data is outside the model's expected input distribution is paramount for givin…