Generative models for discrete random variables

Rianne van den Berg (Microsoft Research Amsterdam)

21-Apr-2022, 16:00-17:00 (23 months ago)

Abstract: n this talk I will discuss how different classes of generative models can be adapted to handle discrete random variables, and how this can be used to connect generative models to downstream tasks such as lossless compression. I will start by discussing normalizing flow models, and the challenges that arise when converting these models that are typically designed for real-valued random variables to discrete random variables. Next, I will demonstrate how denoising diffusion models with discrete state spaces have a rich design space in terms of the noising process, and how this influences the performance of the learned denoising model. Finally, I will show how denoising diffusion models can be connected to autoregressive models, and introduce an autoregressive model with a random generation order.

data structures and algorithmsmachine learningmathematical physicsinformation theoryoptimization and controldata analysis, statistics and probability

Audience: researchers in the topic


Mathematics, Physics and Machine Learning (IST, Lisbon)

Series comments: To receive the series announcements, please register in:
mpml.tecnico.ulisboa.pt
mpml.tecnico.ulisboa.pt/registration
Zoom link: videoconf-colibri.zoom.us/j/91599759679

Organizers: Mário Figueiredo, Tiago Domingos, Francisco Melo, Jose Mourao*, Cláudia Nunes, Yasser Omar, Pedro Alexandre Santos, João Seixas, Cláudia Soares, João Xavier
*contact for this listing

Export talk to