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…
Clustering via Self-Supervised Diffusion
Roy Uziel, Irit Chelly, Oren Freifeld +1
Diffusion models, widely recognized for their success in generative tasks, have not yet been applied to clustering. We introduce Clustering via Diffusion (CLUDI), a self-supervised…
Consistent Amortized Clustering via Generative Flow Networks
Irit Chelly, Roy Uziel, Oren Freifeld +1
Neural models for amortized probabilistic clustering yield samples of cluster labels given a set-structured input, while avoiding lengthy Markov chain runs and the need for explici…
GIF: Generative Interpretable Faces
Partha Ghosh, Pravir Singh Gupta, Roy Uziel +3
Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unreali…
Synthetic-to-Real Domain Adaptation for Lane Detection
Noa Garnett, Roy Uziel, Netalee Efrat +1
Accurate lane detection, a crucial enabler for autonomous driving, currently relies on obtaining a large and diverse labeled training dataset. In this work, we explore learning fro…