3 papers
stat.ML2026
Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
Grigory Bartosh, Teodora Pandeva, Sushrut Karmalkar +1
Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete diffusion typically parameterizes…
cs.LG2025
Parallel Sampling from Masked Diffusion Models via Conditional Independence Testing
Iskander Azangulov, Teodora Pandeva, Niranjani Prasad +2
Masked diffusion models (MDMs) offer a compelling alternative to autoregressive models (ARMs) for discrete text generation because they enable parallel token sampling, rather than…
stat.ML2025
A Fourier Space Perspective on Diffusion Models
Fabian Falck, Teodora Pandeva, Kiarash Zahirnia +5
Diffusion models are state-of-the-art generative models on data modalities such as images, audio, proteins and materials. These modalities share the property of exponentially decay…