8 citations · 13 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
Robust Multi-view Co-expression Network Inference
Teodora Pandeva, Martijs Jonker, Leendert Hamoen +2
Unraveling the co-expression of genes across studies enhances the understanding of cellular processes. Inferring gene co-expression networks from transcriptome data presents many c…
Deep anytime-valid hypothesis testing
Teodora Pandeva, Patrick Forré, Aaditya Ramdas +1
We propose a general framework for constructing powerful, sequential hypothesis tests for a large class of nonparametric testing problems. The null hypothesis for these problems is…
E-Valuating Classifier Two-Sample Tests
Teodora Pandeva, Tim Bakker, Christian A. Naesseth +1
We introduce a powerful deep classifier two-sample test for high-dimensional data based on E-values, called E-value Classifier Two-Sample Test (E-C2ST). Our test combines ideas fro…
Multi-View Independent Component Analysis with Shared and Individual Sources
Teodora Pandeva, Patrick Forré
Independent component analysis (ICA) is a blind source separation method for linear disentanglement of independent latent sources from observed data. We investigate the special set…
MMGAN: Generative Adversarial Networks for Multi-Modal Distributions
Teodora Pandeva, Matthias Schubert
Over the past years, Generative Adversarial Networks (GANs) have shown a remarkable generation performance especially in image synthesis. Unfortunately, they are also known for hav…