6 citations · 19 across the 12 of their papers we have counts for
6 papers · 1 filter
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…
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…
Pulling the Carpet Below the Learner's Feet: Genetic Algorithm To Learn Ensemble Machine Learning Model During Concept Drift
Teddy Lazebnik
Data-driven models, in general, and machine learning (ML) models, in particular, have gained popularity over recent years with an increased usage of such models across the scientif…
Global Lightning-Ignited Wildfires Prediction and Climate Change Projections based on Explainable Machine Learning Models
Assaf Shmuel, Teddy Lazebnik, Oren Glickman +2
Wildfires pose a significant natural disaster risk to populations and contribute to accelerated climate change. As wildfires are also affected by climate change, extreme wildfires…
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
The analysis of tabular datasets is highly prevalent both in scientific research and real-world applications of Machine Learning (ML). Unlike many other ML tasks, Deep Learning (DL…
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…