Steering masked discrete diffusion models via discrete denoising posterior prediction
Generative modeling of discrete data underlies important applications spanning text-based
agents like ChatGPT to the design of the very building blocks of life in protein sequences …
agents like ChatGPT to the design of the very building blocks of life in protein sequences …
How discrete and continuous diffusion meet: Comprehensive analysis of discrete diffusion models via a stochastic integral framework
Discrete diffusion models have gained increasing attention for their ability to model complex
distributions with tractable sampling and inference. However, the error analysis for discrete …
distributions with tractable sampling and inference. However, the error analysis for discrete …
Reliable machine learning models in genomic medicine using conformal prediction
Machine learning and genomic medicine are the mainstays of research in delivering
personalized healthcare services for disease diagnosis, risk stratification, tailored treatment …
personalized healthcare services for disease diagnosis, risk stratification, tailored treatment …
Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension
Peptides, short chains of amino acids, interact with target proteins, making them a unique
class of protein-based therapeutics for treating human diseases. Recently, deep generative …
class of protein-based therapeutics for treating human diseases. Recently, deep generative …
Wasserstein Flow Matching: Generative modeling over families of distributions
Generative modeling typically concerns the transport of a single source distribution to a
single target distribution by learning (ie, regressing onto) simple probability flows. However …
single target distribution by learning (ie, regressing onto) simple probability flows. However …
Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data
We introduce a novel generative model for the representation of joint probability distributions
of a possibly large number of discrete random variables. The approach uses measure …
of a possibly large number of discrete random variables. The approach uses measure …
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms
Discrete diffusion models have emerged as a powerful generative modeling framework for
discrete data with successful applications spanning from text generation to image synthesis …
discrete data with successful applications spanning from text generation to image synthesis …