10 papers
Improving Deep Tabular Learning
Sivan Sarafian, Yehudit Aperstein
Tabular data remain a dominant form of real-world information but pose persistent challenges for deep learning due to heterogeneous feature types, lack of natural structure, and li…
From Fuzzy Speech to Medical Insight: Benchmarking LLMs on Noisy Patient Narratives
Eden Mama, Liel Sheri, Yehudit Aperstein +1
The widespread adoption of large language models (LLMs) in healthcare raises critical questions about their ability to interpret patient-generated narratives, which are often infor…
When Curiosity Signals Danger: Predicting Health Crises Through Online Medication Inquiries
Dvora Goncharok, Arbel Shifman, Alexander Apartsin +1
Online medical forums are a rich and underutilized source of insight into patient concerns, especially regarding medication use. Some of the many questions users pose may signal co…
An Interpretable Benchmark for Clickbait Detection and Tactic Attribution
Lihi Nofar, Tomer Portal, Aviv Elbaz +2
The proliferation of clickbait headlines poses significant challenges to the credibility of information and user trust in digital media. While recent advances in machine learning h…
Multi-pathology Chest X-ray Classification with Rejection Mechanisms
Yehudit Aperstein, Amit Tzahar, Alon Gottlib +3
Overconfidence in deep learning models poses a significant risk in high-stakes medical imaging tasks, particularly in multi-label classification of chest X-rays, where multiple co-…
Enhancing Classification of Streaming Data with Image Distillation
Rwad Khatib, Yehudit Aperstein
This study tackles the challenge of efficiently classifying streaming data in envi-ronments with limited memory and computational resources. It delves into the application of data…