1 citations · 2 across the 8 of their papers we have counts for
3 papers · 1 filter
RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering
Matthew Hull, Haoran Wang, Matthew Lau +8
Differentiable rendering techniques like Gaussian Splatting and Neural Radiance Fields have become powerful tools for generating high-fidelity models of 3D objects and scenes. Thei…
Non-Robust Features are Not Always Useful in One-Class Classification
Matthew Lau, Haoran Wang, Alec Helbling +5
The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that…
Revisiting Non-separable Binary Classification and its Applications in Anomaly Detection
Matthew Lau, Ismaila Seck, Athanasios P Meliopoulos +2
The inability to linearly classify XOR has motivated much of deep learning. We revisit this age-old problem and show that linear classification of XOR is indeed possible. Instead o…