42 citations · 52 across the 9 of their papers we have counts for
11 papers
Gumbel Straight Flow: Distilling Autoregressive Models into One-step Flow Maps
Yeongmin Kim, Arnaud Doucet, Andrew Campbell +3
We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive language (AR) model. We theoretica…
Simplex Diffusion Models
Justin Deschenaux, Alexandre Galashov, Andrew Campbell +4
Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state. This iterative refinement has not yet carried over to…
DiffusionGemma Technical Report
DiffusionGemma Team, Adrien Ali Taïga, James Assiene +41
We introduce DiffusionGemma, an experimental open-weight language model that uses discrete diffusion to generate text at exceptionally high speed. Rather than decoding one token at…
Toward World Modeling of Physiological Signals with Chaos-Theoretic Balancing and Latent Dynamics
Yunfei Luo, Xi Chen, Yuliang Chen +8
Physiological time series signals reflect complex, multi-scale dynamical processes of the human body. Existing modeling studies focus on static tasks such as classification, event…
Self-Speculative Masked Diffusions
Andrew Campbell, Valentin De Bortoli, Jiaxin Shi +1
We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate…
Score-Optimal Diffusion Schedules
Christopher Williams, Andrew Campbell, Arnaud Doucet +1
Denoising diffusion models (DDMs) offer a flexible framework for sampling from high dimensional data distributions. DDMs generate a path of probability distributions interpolating…
Trans-Dimensional Generative Modeling via Jump Diffusion Models
Andrew Campbell, William Harvey, Christian Weilbach +3
We propose a new class of generative models that naturally handle data of varying dimensionality by jointly modeling the state and dimension of each datapoint. The generative proce…
Diffusion Schrödinger Bridge Matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell +1
Solving transport problems, i.e. finding a map transporting one given distribution to another, has numerous applications in machine learning. Novel mass transport methods motivated…
normflows: A PyTorch Package for Normalizing Flows
Vincent Stimper, David Liu, Andrew Campbell +4
Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of inve…
A Continuous Time Framework for Discrete Denoising Models
Andrew Campbell, Joe Benton, Valentin De Bortoli +3
We provide the first complete continuous time framework for denoising diffusion models of discrete data. This is achieved by formulating the forward noising process and correspondi…
Online Variational Filtering and Parameter Learning
Andrew Campbell, Yuyang Shi, Tom Rainforth +1
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. A…