Search Results for author:
Found 11 papers, 9 papers with code
Diffusion Models for Video Prediction and Infilling
By varying the mask we condition on, the model is able to perform video prediction, infilling, and upsampling.
Ranked #2 on
Video Generation
on BAIR Robot Pushing
Few-Shot Diffusion Models
At test time, the model is able to generate samples from previously unseen classes conditioned on as few as 5 samples from that class.
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Argmax Flows are defined by a composition of a continuous distribution (such as a normalizing flow), and an argmax function.
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC
Hybrid Monte Carlo is a powerful Markov Chain Monte Carlo method for sampling from complex continuous distributions.
Argmax Flows: Learning Categorical Distributions with Normalizing Flows
This paper introduces a new method to define and train continuous distributions such as normalizing flows directly on categorical data, for example text and image segmentation.
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows
Normalizing flows and variational autoencoders are powerful generative models that can represent complicated density functions.
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow
Flow models have recently made great progress at modeling ordinal discrete data such as images and audio.
SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
Uncertainty estimation in large deep-learning models is a computationally challenging task, where it is difficult to form even a Gaussian approximation to the posterior distribution.
Fast yet Simple Natural-Gradient Descent for Variational Inference in Complex Models
Bayesian inference plays an important role in advancing machine learning, but faces computational challenges when applied to complex models such as deep neural networks.
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Uncertainty computation in deep learning is essential to design robust and reliable systems.
Variational Adaptive-Newton Method for Explorative Learning
We present the Variational Adaptive Newton (VAN) method which is a black-box optimization method especially suitable for explorative-learning tasks such as active learning and reinforcement learning.
