8 citations · 9 across the 32 of their papers we have counts for
15 papers · 1 filter
Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
Noam Tsfaty, Avishai Weizman, Liav Cohen +2
We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and t…
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…
From Joy to Fear: A Benchmark of Emotion Estimation in Pop Song Lyrics
Shay Dahary, Avi Edana, Alexander Apartsin +1
The emotional content of song lyrics plays a pivotal role in shaping listener experiences and influencing musical preferences. This paper investigates the task of multi-label emoti…
Do Large Language Models Need Intent? Revisiting Response Generation Strategies for Service Assistant
Inbal Bolshinsky, Shani Kupiec, Almog Sasson +2
In the era of conversational AI, generating accurate and contextually appropriate service responses remains a critical challenge. A central question remains: Is explicit intent rec…
Code Review Without Borders: Evaluating Synthetic vs. Real Data for Review Recommendation
Yogev Cohen, Dudi Ohayon, Romy Somkin +2
Automating the decision of whether a code change requires manual review is vital for maintaining software quality in modern development workflows. However, the emergence of new pro…
Stitching the Story: Creating Panoramic Incident Summaries from Body-Worn Footage
Dor Cohen, Inga Efrosman, Yehudit Aperstein +1
First responders widely adopt body-worn cameras to document incident scenes and support post-event analysis. However, reviewing lengthy video footage is impractical in time-critica…
Beyond Words: Interjection Classification for Improved Human-Computer Interaction
Yaniv Goren, Yuval Cohen, Alexander Apartsin +1
In the realm of human-computer interaction, fostering a natural dialogue between humans and machines is paramount. A key, often overlooked, component of this dialogue is the use of…
CalexNet: Soft Cascade-Aligned Training and Calibration for Lightweight Early-Exit Branches
Yehudit Aperstein, Alexander Apartsin
Early-exit cascades over a frozen convolutional backbone enable adaptive inference but suffer from three sources of train-inference mismatch: branches train on samples they will ne…
Explainable Semantic Text Relations: A Question-Answering Framework for Comparing Document Content
Yehudit Aperstein, Alon Gottlib, Gal Benita +1
Understanding semantic relations between two texts is crucial for many information and document management tasks, in which one must determine whether the content fully overlaps, is…
PTEENet: Post-Trained Early-Exit Neural Networks Augmentation for Inference Cost Optimization
Assaf Lahiany, Yehudit Aperstein
For many practical applications, a high computational cost of inference over deep network architectures might be unacceptable. A small degradation in the overall inference accuracy…