1 citations · 1 across the 14 of their papers we have counts for
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Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection
Willian T. Lunardi, Samridha Shrestha, Martin Andreoni
Out-of-distribution (OOD) detection for time-series data remains comparatively underexplored compared to vision and language, with a limited principled understanding of how supervi…
Contrastive Representation Modeling for Anomaly Detection
Willian T. Lunardi, Abdulrahman Banabila, Dania Herzalla +1
Distance-based anomaly detection methods rely on compact in-distribution (ID) embeddings that are well separated from anomalies. However, conventional contrastive learning strategi…
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…