no code implementations • 14 Feb 2023 • Jae Hyun Lim, Nikola B. Kovachki, Ricardo Baptista, Christopher Beckham, Kamyar Azizzadenesheli, Jean Kossaifi, Vikram Voleti, Jiaming Song, Karsten Kreis, Jan Kautz, Christopher Pal, Arash Vahdat, Anima Anandkumar
They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by denoising.
1 code implementation • NeurIPS 2021 • Chin-wei Huang, Jae Hyun Lim, Aaron Courville
Under this framework, we show that minimizing the score-matching loss is equivalent to maximizing a lower bound of the likelihood of the plug-in reverse SDE proposed by Song et al. (2021), bridging the theoretical gap.
no code implementations • pproximateinference AABI Symposium 2021 • Jae Hyun Lim, Chin-wei Huang, Aaron Courville, Christopher Pal
In this work, we propose Bijective-Contrastive Estimation (BCE), a classification-based learning criterion for energy-based models.
2 code implementations • ICML 2020 • Jae Hyun Lim, Aaron Courville, Christopher Pal, Chin-wei Huang
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.
2 code implementations • NeurIPS 2019 • Jae Hyun Lim, Pedro O. Pinheiro, Negar Rostamzadeh, Christopher Pal, Sungjin Ahn
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 looking at and touching objects.
6 code implementations • 8 May 2017 • 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.