1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
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…
CRoCoDiL: Continuous and Robust Conditioned Diffusion for Language
Roy Uziel, Omer Belhasin, Itay Levy +4
Masked Diffusion Models (MDMs) provide an efficient non-causal alternative to autoregressive generation but often struggle with token dependencies and semantic incoherence due to t…
Learn from Your Mistakes: Self-Correcting Masked Diffusion Models
Yair Schiff, Omer Belhasin, Roy Uziel +6
Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models, enabling parallel token generation while achieving competitive performance. Despite…
Advancing Image Classification with Discrete Diffusion Classification Modeling
Omer Belhasin, Shelly Golan, Ran El-Yaniv +1
Image classification is a well-studied task in computer vision, and yet it remains challenging under high-uncertainty conditions, such as when input images are corrupted or trainin…
Uncertainty-Aware PPG-2-ECG for Enhanced Cardiovascular Diagnosis using Diffusion Models
Omer Belhasin, Idan Kligvasser, George Leifman +7
Analyzing the cardiovascular system condition via Electrocardiography (ECG) is a common and highly effective approach, and it has been practiced and perfected over many decades. EC…
Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
Omer Belhasin, Yaniv Romano, Daniel Freedman +2
Uncertainty quantification for inverse problems in imaging has drawn much attention lately. Existing approaches towards this task define uncertainty regions based on probable value…
TransBoost: Improving the Best ImageNet Performance using Deep Transduction
Omer Belhasin, Guy Bar-Shalom, Ran El-Yaniv
This paper deals with deep transductive learning, and proposes TransBoost as a procedure for fine-tuning any deep neural model to improve its performance on any (unlabeled) test se…