11 citations · 11 across the 2 of their papers we have counts for
4 papers
Transition Matching: Scalable and Flexible Generative Modeling
Neta Shaul, Uriel Singer, Itai Gat +1
Diffusion and flow matching models have significantly advanced media generation, yet their design space is well-explored, somewhat limiting further improvements. Concurrently, auto…
Corrector Sampling in Language Models
Itai Gat, Neta Shaul, Uriel Singer +1
Autoregressive language models accumulate errors due to their fixed, irrevocable left-to-right token generation. To address this, we propose a new sampling method called Resample-P…
Flow Matching Guide and Code
Yaron Lipman, Marton Havasi, Peter Holderrieth +7
Flow Matching (FM) is a recent framework for generative modeling that has achieved state-of-the-art performance across various domains, including image, video, audio, speech, and b…
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective
Neta Shaul, Itai Gat, Marton Havasi +6
The design space of discrete-space diffusion or flow generative models are significantly less well-understood than their continuous-space counterparts, with many works focusing onl…