92 citations · 183 across the 23 of their papers we have counts for
9 papers · 1 filter
Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy
Thi Kieu Khanh Ho, Thomas Lai, Petr Klimes +5
Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizure freedom rates remain below 5…
Video-Based Prediction of In-Flight Particle Characteristics in Atmospheric Plasma Spraying
Abhijeet Praveen, Sareh Soleimani, Cormac Cureton +4
Atmospheric plasma spraying (APS) is a widely used coating process in which in-flight particle temperature and velocity strongly influence coating quality. However, these particle…
Unsupervised Continual Clustering via Forward-Backward Knowledge Distillation
Mohammadreza Sadeghi, Sareh Soleimani, Zihan Wang +1
Unsupervised Continual Learning (UCL) aims to enable neural networks to learn sequential tasks without labels or access to past data. A major challenge in this setting is Catastrop…
TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
Cormac Cureton, Narges Armanfard
Prior-Data Fitted networks (PFNs) have been very successful in tabular contexts, handling prediction tasks in context. However, they are designed for single-task inference, meaning…
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…
Sharp Concentration Inequalities: Phase Transition and Mixing of Orlicz Tails with Variance
Yinan Shen, Jinchi Lv
In this work, we investigate how to develop sharp concentration inequalities for sub-Weibull random variables, including sub-Gaussian and sub-exponential distributions. Although th…
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…
ARTA: Adversarial-Robust Multivariate Time--Series Anomaly Detection via Sparsity-Constrained Perturbations
Hadi Hojjati, Narges Armanfard
Time-series anomaly detection (TSAD) is a critical component in monitoring complex systems, yet modern deep learning-based detectors are often highly sensitive to localized input c…