2 citations · 3 across the 12 of their papers we have counts for
12 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…
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…
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…
Variational Control for Guidance in Diffusion Models
Kushagra Pandey, Farrin Marouf Sofian, Felix Draxler +2
Diffusion models exhibit excellent sample quality, but existing guidance methods often require additional model training or are limited to specific tasks. We revisit guidance in di…
Heavy-Tailed Diffusion Models
Kushagra Pandey, Jaideep Pathak, Yilun Xu +4
Diffusion models achieve state-of-the-art generation quality across many applications, but their ability to capture rare or extreme events in heavy-tailed distributions remains unc…
Fast Samplers for Inverse Problems in Iterative Refinement Models
Kushagra Pandey, Ruihan Yang, Stephan Mandt
Constructing fast samplers for unconditional diffusion and flow-matching models has received much attention recently; however, existing methods for solving inverse problems, such a…
Towards Fast Stochastic Sampling in Diffusion Generative Models
Kushagra Pandey, Maja Rudolph, Stephan Mandt
Diffusion models suffer from slow sample generation at inference time. Despite recent efforts, improving the sampling efficiency of stochastic samplers for diffusion models remains…
Efficient Integrators for Diffusion Generative Models
Kushagra Pandey, Maja Rudolph, Stephan Mandt
Diffusion models suffer from slow sample generation at inference time. Therefore, developing a principled framework for fast deterministic/stochastic sampling for a broader class o…
A Complete Recipe for Diffusion Generative Models
Kushagra Pandey, Stephan Mandt
Score-based Generative Models (SGMs) have demonstrated exceptional synthesis outcomes across various tasks. However, the current design landscape of the forward diffusion process r…