activity
20182024
most citedMDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

9 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2024

Streamlining Conformal Information Retrieval via Score Refinement

Yotam Intrator, Ori Kelner, Regev Cohen +3

Information retrieval (IR) methods, like retrieval augmented generation, are fundamental to modern applications but often lack statistical guarantees. Conformal prediction addresse…

eess.IV2024

Predicting Generalization of AI Colonoscopy Models to Unseen Data

Joel Shor, Carson McNeil, Yotam Intrator +15

: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on…

cs.CL2024★ 1 cited

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

Yotam Intrator, Matan Halfon, Roman Goldenberg +5

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the wides…

cs.CV2023★ 8 cited

Self-Supervised Polyp Re-Identification in Colonoscopy

Yotam Intrator, Natalie Aizenberg, Amir Livne +2

Computer-aided polyp detection (CADe) is becoming a standard, integral part of any modern colonoscopy system. A typical colonoscopy CADe detects a polyp in a single frame and does…

cs.LG2018★ 9 cited

MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks

Yotam Intrator, Gilad Katz, Asaf Shabtai

Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient…