9 papers
Variational Test-time Optimization for Diffusion Synchronization
Hyunsoo Lee, Farrin Marouf Sofian, Kushagra Pandey +1
Collaborative generation, which coordinates multiple diffusion trajectories to extend the capabilities of pretrained priors, has emerged as a powerful paradigm for extending the ap…
Uncertainty Estimation for Molecular Diffusion Models
Paul Seij, Christian A. Naesseth, Stephan Mandt +1
Diffusion models have seen wide adoption for 3D molecular generation, yet they offer no principled signal of when a generated molecule is likely to be of low quality. We propose a…
Skipping the Zeros in Diffusion Models for Sparse Data Generation
Phil Sidney Ostheimer, Mayank Nagda, Andriy Balinskyy +6
Diffusion models (DMs) excel on dense continuous data, but are not designed for sparse continuous data. They do not model exact zeros that represent the deliberate absence of a sig…
Hierarchical Variational Policies for Reward-Guided Diffusion
Kushagra Pandey, Farrin Marouf Sofian, Jan Niklas Groeneveld +2
Adapting pretrained diffusion models to downstream objectives such as inverse problems often requires expensive test-time guidance or optimization. We propose a principled framewor…
Control-Augmented Autoregressive Diffusion for Data Assimilation
Prakhar Srivastava, Farrin Marouf Sofian, Francesco Immorlano +2
Despite advances in test-time scaling and diffusion finetuning, guidance for Auto-Regressive Diffusion Models (ARDMs) remains underexplored. We introduce an amortized framework tha…
Parallel Token Prediction for Language Models
Felix Draxler, Justus Will, Farrin Marouf Sofian +3
Autoregressive decoding in language models is inherently slow, generating only one token per forward pass. We propose Parallel Token Prediction (PTP), a general-purpose framework f…