173 citations · 383 across the 11 of their papers we have counts for
5 papers · 1 filter
Discrete Neural Flow Samplers with Locally Equivariant Transformer
Zijing Ou, Ruixiang Zhang, Yingzhen Li
Sampling from unnormalised discrete distributions is a fundamental problem across various domains. While Markov chain Monte Carlo offers a principled approach, it often suffers fro…
Robust and Controllable Object-Centric Learning through Energy-based Models
Ruixiang Zhang, Tong Che, Boris Ivanovic +4
Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability to decompose low-level observations into discrete objects allows us to build a…
Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective
Ruixiang Zhang, Masanori Koyama, Katsuhiko Ishiguro
Learning controllable and generalizable representation of multivariate data with desired structural properties remains a fundamental problem in machine learning. In this paper, we…
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein +4
We show that the sum of the implicit generator log-density of a GAN with the logit score of the discriminator defines an energy function which yields the true data densi…
Perceptual Generative Autoencoders
Zijun Zhang, Ruixiang Zhang, Zongpeng Li +2
Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of data can be much lower than the ambient dim…