92 citations · 183 across the 23 of their papers we have counts for
25 papers
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…
MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data
Dimitrios Sinodinos, Jack Yi Wei, Narges Armanfard
Tabular data is the most abundant data type in the world, powering systems in finance, healthcare, e-commerce, and beyond. As tabular datasets grow and span multiple related target…
Zero-Shot Anomaly Detection with Dual-Branch Prompt Selection
Zihan Wang, Samira Ebrahimi Kahou, Narges Armanfard
Zero-shot anomaly detection (ZSAD) enables identifying and localizing defects in unseen categories by relying solely on generalizable features rather than requiring any labeled exa…
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…
Forward-Backward Knowledge Distillation for Continual Clustering
Mohammadreza Sadeghi, Zihan Wang, Narges Armanfard
Unsupervised Continual Learning (UCL) is a burgeoning field in machine learning, focusing on enabling neural networks to sequentially learn tasks without explicit label information…
Deep Clustering with Self-Supervision using Pairwise Similarities
Mohammadreza Sadeghi, Narges Armanfard
Deep clustering incorporates embedding into clustering to find a lower-dimensional space appropriate for clustering. In this paper, we propose a novel deep clustering framework wit…
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Ali Karami, Thi Kieu Khanh Ho, Narges Armanfard
Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enables operators to promptly detect suspicio…
Cross-Task Affinity Learning for Multitask Dense Scene Predictions
Dimitrios Sinodinos, Narges Armanfard
Multitask learning (MTL) has become prominent for its ability to predict multiple tasks jointly, achieving better per-task performance with fewer parameters than single-task learni…
Open-Set Multivariate Time-Series Anomaly Detection
Thomas Lai, Thi Kieu Khanh Ho, Narges Armanfard
Numerous methods for time-series anomaly detection (TSAD) have emerged in recent years, most of which are unsupervised and assume that only normal samples are available during the…
Language-Guided Reinforcement Learning for Hard Attention in Few-Shot Learning
Bahareh Nikpour, Narges Armanfard
Attention mechanisms have demonstrated significant potential in enhancing learning models by identifying key portions of input data, particularly in scenarios with limited training…
Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models
Thi Kieu Khanh Ho, Narges Armanfard
Mainstream unsupervised anomaly detection algorithms often excel in academic datasets, yet their real-world performance is restricted due to the controlled experimental conditions…
Graph Anomaly Detection in Time Series: A Survey
Thi Kieu Khanh Ho, Ali Karami, Narges Armanfard
With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (T…
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 Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis
Thi Kieu Khanh Ho, Narges Armanfard
Electroencephalogram (EEG) signals are effective tools towards seizure analysis where one of the most important challenges is accurate detection of seizure events and brain regions…
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…
Attentive Task Interaction Network for Multi-Task Learning
Dimitrios Sinodinos, Narges Armanfard
Multitask learning (MTL) has recently gained a lot of popularity as a learning paradigm that can lead to improved per-task performance while also using fewer per-task model paramet…