4 papers
EngineAD: A Real-World Vehicle Engine Anomaly Detection Dataset
Hadi Hojjati, Christopher Roth, Rory Woods +2
The progress of Anomaly Detection (AD) in safety-critical domains, such as transportation, is severely constrained by the lack of large-scale, real-world benchmarks. To address thi…
Collision-Aware Vision-Language Learning for End-to-End Driving with Multimodal Infraction Datasets
Alex Koran, Dimitrios Sinodinos, Hadi Hojjati +3
High infraction rates remain the primary bottleneck for end-to-end (E2E) autonomous driving, as evidenced by the low driving scores on the CARLA Leaderboard. Despite collision-rela…
Multitask-Informed Prior for In-Context Learning on Tabular Data: Application to Steel Property Prediction
Dimitrios Sinodinos, Bahareh Nikpour, Jack Yi Wei +5
Accurate prediction of mechanical properties of steel during hot rolling processes, such as Thin Slab Direct Rolling (TSDR), remains challenging due to complex interactions among c…
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
Jack Yi Wei, Narges Armanfard
Anomaly detection on tabular data is commonly studied under three supervision regimes, including one-class settings that assume access to anomaly-free training samples, fully unsup…