3 citations · 7 across the 5 of their papers we have counts for
6 papers
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu, Wan-Yi Lin, Devin Willmott +4
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data…
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Ameya Joshi, Minh Pham, Minsu Cho +4
Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require aug…
You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries
Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami +2
Researchers have repeatedly shown that it is possible to craft adversarial attacks on deep classifiers (small perturbations that significantly change the class label), even in the…
Provably robust deep generative models
Filipe Condessa, Zico Kolter
Recent work in adversarial attacks has developed provably robust methods for training deep neural network classifiers. However, although they are often mentioned in the context of…
Robust hyperspectral image classification with rejection fields
Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyperspectral image classification is generally an ill-posed imag…
SegSALSA-STR: A convex formulation to supervised hyperspectral image segmentation using hidden fields and structure tensor regularization
Filipe Condessa, Jose Bioucas-Dias, Jelena Kovacevic
We present a supervised hyperspectral image segmentation algorithm based on a convex formulation of a marginal maximum a posteriori segmentation with hidden fields and structure te…