Search Results for author:
Found 11 papers, 1 papers with code
Imagen Video: High Definition Video Generation with Diffusion Models
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models.
Ranked #1 on
Video Generation
on LAION-400M
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding.
Ranked #17 on
Text-to-Image Generation
on MS COCO
(using extra training data)
Deblurring via Stochastic Refinement
Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input.
Neural Distributed Source Coding
Distributed source coding (DSC) is the task of encoding an input in the absence of correlated side information that is only available to the decoder.
Model-Based Deep Learning
We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
Compressed Sensing with Invertible Generative Models and Dependent Noise
We study image inverse problems with invertible generative priors, specifically normalizing flow models.
Approximate Probabilistic Inference with Composed Flows
We study the problem of probabilistic inference on the joint distribution defined by a normalizing flow model.
Solving Inverse Problems with a Flow-based Noise Model
We study image inverse problems with a normalizing flow prior.
Composing Normalizing Flows for Inverse Problems
We approach this problem as a task of conditional inference on the pre-trained unconditional flow model.
Training Variational Autoencoders with Buffered Stochastic Variational Inference
The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets.
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
People seem to build simple models that are easy to learn to support planning and strategic exploration.
