209 citations · 216 across the 28 of their papers we have counts for
7 papers · 1 filter
CACFG: Curvature-Aware Classifier-Free Guidance and Optimal Control
Max Collins, Dasith de Silva Edirimuni, Jordan Vice +2
Diffusion models generate samples by learning to reverse a fixed corruption process, and classifier-free guidance (CFG) is the standard mechanism for conditioning this process on a…
Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
Peiyu Yang, Naveed Akhtar, Jiantong Jiang +1
The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models…
NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusion
Max Collins, Jordan Vice, Tim French +1
Adversarial samples exploit irregularities in the manifold `learned' by deep learning models to cause misclassifications. The study of these adversarial samples provides insight in…
On Transfer-based Universal Attacks in Pure Black-box Setting
Mohammad A. A. K. Jalwana, Naveed Akhtar, Ajmal Mian +2
Despite their impressive performance, deep visual models are susceptible to transferable black-box adversarial attacks. Principally, these attacks craft perturbations in a target m…
FastBO: Fast HPO and NAS with Adaptive Fidelity Identification
Jiantong Jiang, Ajmal Mian
Hyperparameter optimization (HPO) and neural architecture search (NAS) are powerful in attaining state-of-the-art machine learning models, with Bayesian optimization (BO) standing…
Regulating Model Reliance on Non-Robust Features by Smoothing Input Marginal Density
Peiyu Yang, Naveed Akhtar, Mubarak Shah +1
Trustworthy machine learning necessitates meticulous regulation of model reliance on non-robust features. We propose a framework to delineate and regulate such features by attribut…
Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
Jiantong Jiang, Zeyi Wen, Peiyu Yang +2
Probabilistic graphical models (PGMs) serve as a powerful framework for modeling complex systems with uncertainty and extracting valuable insights from data. However, users face ch…