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20222025
most citedSE(3) diffusion model with application to protein backbone generation

71 citations · 82 across the 5 of their papers we have counts for

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cs.LG20251 cited

Distributional Diffusion Models with Scoring Rules

Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli +4

Diffusion models generate high-quality synthetic data. They operate by defining a continuous-time forward process which gradually adds Gaussian noise to data until fully corrupted.…

cs.LG2025

Accelerated Diffusion Models via Speculative Sampling

Valentin De Bortoli, Alexandre Galashov, Arthur Gretton +1

Speculative sampling is a popular technique for accelerating inference in Large Language Models by generating candidate tokens using a fast draft model and accepting or rejecting t…

cs.LG20231 cited

Unbalanced Diffusion Schrödinger Bridge

Matteo Pariset, Ya-Ping Hsieh, Charlotte Bunne +2

Schrödinger bridges (SBs) provide an elegant framework for modeling the temporal evolution of populations in physical, chemical, or biological systems. Such natural processes are c…

cs.LG202371 cited

SE(3) diffusion model with application to protein backbone generation

Jason Yim, Brian L. Trippe, Valentin De Bortoli +4

The design of novel protein structures remains a challenge in protein engineering for applications across biomedicine and chemistry. In this line of work, a diffusion model over ri…

cs.LG202210 cited

Wavelet Score-Based Generative Modeling

Florentin Guth, Simon Coste, Valentin De Bortoli +1

Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient…