63 citations · 116 across the 5 of their papers we have counts for
5 papers
Score-based Diffusion Models in Function Space
Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista +10
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a r…
A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Chin-Wei Huang, Jae Hyun Lim, Aaron Courville
Discrete-time diffusion-based generative models and score matching methods have shown promising results in modeling high-dimensional image data. Recently, Song et al. (2021) show t…
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Jae Hyun Lim, Aaron Courville, Christopher Pal +1
Entropy is ubiquitous in machine learning, but it is in general intractable to compute the entropy of the distribution of an arbitrary continuous random variable. In this paper, we…
Neural Multisensory Scene Inference
Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh +2
For embodied agents to infer representations of the underlying 3D physical world they inhabit, they should efficiently combine multisensory cues from numerous trials, e.g., by look…
Geometric GAN
Jae Hyun Lim, Jong Chul Ye
Generative Adversarial Nets (GANs) represent an important milestone for effective generative models, which has inspired numerous variants seemingly different from each other. One o…