activity
20192026
most citedMLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning Approach

4 citations · 9 across the 15 of their papers we have counts for

collaborators

17 papers

cs.LG2026

Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models

Yair Schiff, Omer Belhasin, Roy Uziel +7

Recent works on continuous diffusion for discrete data have demonstrated performance on par with comparable discrete diffusion models. However, these continuous counterparts lack k…

cs.CL2026

LiLiCorr: Lightweight Likelihood Correlation of Parallel Drafts for Speculative Decoding

Matan Rusanovsky, Yoav Miron, Roy Uziel +5

Speculative decoding accelerates language-model inference by drafting future tokens the target model verifies in parallel. A diffusion-style drafter such as DFlash drafts an entire…

cs.CV2026

Optimal Transport Flow Matching by Design

Shimon Malnick, Matan Rusanovsky, Ohad Fried +1

Flow matching models learn to transport samples from a simple prior distribution to a complex data distribution. When prior-data pairs are coupled via optimal transport (OT), the l…

cs.CV2025

Talking Points: Describing and Localizing Pixels

Matan Rusanovsky, Shimon Malnick, Shai Avidan

Vision-language models have achieved remarkable success in cross-modal understanding. Yet, these models remain limited to object-level or region-level grounding, lacking the capabi…

cs.CV2024

Memories of Forgotten Concepts

Matan Rusanovsky, Shimon Malnick, Amir Jevnisek +2

Diffusion models dominate the space of text-to-image generation, yet they may produce undesirable outputs, including explicit content or private data. To mitigate this, concept abl…

cs.CV2024

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

Matan Rusanovsky, Or Hirschorn, Shai Avidan

Conventional 2D pose estimation models are constrained by their design to specific object categories. This limits their applicability to predefined objects. To overcome these limit…