19 citations · 26 across the 4 of their papers we have counts for
4 papers
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…
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…
Symbolic Regression as Feature Engineering Method for Machine and Deep Learning Regression Tasks
Assaf Shmuel, Oren Glickman, Teddy Lazebnik
In the realm of machine and deep learning regression tasks, the role of effective feature engineering (FE) is pivotal in enhancing model performance. Traditional approaches of FE o…