7 papers
Tighten The Lasso: A Convex Hull Volume-based Anomaly Detection Method
Uri Itai, Asael Bar Ilan, Teddy Lazebnik
Detecting out-of-distribution (OOD) data is a critical task for maintaining model reliability and robustness. In this study, we propose a novel anomaly detection algorithm that lev…
Introducing 'Inside' Out of Distribution
Teddy Lazebnik
Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies primarily focus on extra…
Transforming Norm-based To Graph-based Spatial Representation for Spatio-Temporal Epidemiological Models
Teddy Lazebnik
Pandemics, with their profound societal and economic impacts, pose significant threats to global health, mortality rates, economic stability, and political landscapes. In response…
Interpretable Transformation and Analysis of Timelines through Learning via Surprisability
Osnat Mokryn, Teddy Lazebnik, Hagit Ben Shoshan
The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medica…
An Empirically-parametrized Spatio-Temporal Extended-SIR Model for Combined Dilution and Vaccination Mitigation for Rabies Outbreaks in Wild Jackals
Teddy Lazebnik, Yehuda Samuel, Jonathan Tichon +4
The transmission of zoonotic diseases between animals and humans poses an increasing threat. Rabies is a prominent example with various instances globally, facilitated by a surplus…
Data Augmentation for Deep Learning Regression Tasks by Machine Learning Models
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
Deep learning (DL) models have gained prominence in domains such as computer vision and natural language processing but remain underutilized for regression tasks involving tabular…