6 papers
Sampling from Flow Language Models via Marginal-Conditioned Bridges
Iskander Azangulov, Leo Zhang
Flow Language Models (FLMs) are a recently introduced class of language models which adapt continuous flow matching for one-hot encoded token sequences. Their denoisers have a spec…
Accelerated Parallel Tempering via Neural Transports
Leo Zhang, Peter Potaptchik, Jiajun He +5
Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeti…
CREPE: Controlling Diffusion with Replica Exchange
Jiajun He, Paul Jeha, Peter Potaptchik +5
Inference-time control of diffusion models aims to steer model outputs to satisfy new constraints without retraining. Previous approaches have mostly relied on heuristic guidance o…
Memory-Guided Unified Hardware Accelerator for Mixed-Precision Scientific Computing
Chuanzhen Wang, Leo Zhang, Eric Liu
Recent hardware acceleration advances have enabled powerful specialized accelerators for finite element computations, spiking neural network inference, and sparse tensor operations…
The Cosine Schedule is Fisher-Rao-Optimal for Masked Discrete Diffusion Models
Leo Zhang, Saifuddin Syed
In this work, we study the problem of choosing the discretisation schedule for sampling from masked discrete diffusion models in terms of the information geometry of the induced pr…
Metric Flow Matching for Smooth Interpolations on the Data Manifold
Kacper KapuÅniak, Peter Potaptchik, Teodora Reu +5
Matching objectives underpin the success of modern generative models and rely on constructing conditional paths that transform a source distribution into a target distribution. Des…