3 citations · 4 across the 10 of their papers we have counts for
3 papers · 1 filter
Exploiting Inter-Sample Information for Long-tailed Out-of-Distribution Detection
Nimeshika Udayangani, Hadi M. Dolatabadi, Sarah Erfani +1
Detecting out-of-distribution (OOD) data is essential for safe deployment of deep neural networks (DNNs). This problem becomes particularly challenging in the presence of long-tail…
Geometry-Guided Adversarial Prompt Detection via Curvature and Local Intrinsic Dimension
Canaan Yung, Hanxun Huang, Christopher Leckie +1
Adversarial prompts are capable of jailbreaking frontier large language models (LLMs) and inducing undesirable behaviours, posing a significant obstacle to their safe deployment. C…
HALO: Robust Out-of-Distribution Detection via Joint Optimisation
Hugo Lyons Keenan, Sarah Erfani, Christopher Leckie
Effective out-of-distribution (OOD) detection is crucial for the safe deployment of machine learning models in real-world scenarios. However, recent work has shown that OOD detecti…