92 citations · 164 across the 7 of their papers we have counts for
7 papers
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…
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…
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection
Alex Koran, Hadi Hojjati, Narges Armanfard
Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improvements in several papers, the practical app…
C3: Cross-instance guided Contrastive Clustering
Mohammadreza Sadeghi, Hadi Hojjati, Narges Armanfard
Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent ad…
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook
Hadi Hojjati, Thi Kieu Khanh Ho, Narges Armanfard
Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity, finance, and healthcare, by identifying patterns or events that deviate from normal behavio…
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection
Hadi Hojjati, Narges Armanfard
Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers…