2.1k citations · 2.5k across the 9 of their papers we have counts for
10 papers
Model-Based Deep Learning
Nir Shlezinger, Yonina C. Eldar
Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physic…
Imagen Video: High Definition Video Generation with Diffusion Models
Jonathan Ho, William Chan, Chitwan Saharia +8
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos…
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena +11
We present Imagen, a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding. Imagen builds on the power of large tran…
Deblurring via Stochastic Refinement
Jay Whang, Mauricio Delbracio, Hossein Talebi +3
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean ima…
Neural Distributed Source Coding
Jay Whang, Alliot Nagle, Anish Acharya +2
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. Remarkably, Slepian and Wolf s…
Model-Based Deep Learning
Nir Shlezinger, Jay Whang, Yonina C. Eldar +1
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations th…
Solving Inverse Problems with a Flow-based Noise Model
Jay Whang, Qi Lei, Alexandros G. Dimakis
We study image inverse problems with a normalizing flow prior. Our formulation views the solution as the maximum a posteriori estimate of the image conditioned on the measurements.…
Composing Normalizing Flows for Inverse Problems
Jay Whang, Erik M. Lindgren, Alexandros G. Dimakis
Given an inverse problem with a normalizing flow prior, we wish to estimate the distribution of the underlying signal conditioned on the observations. We approach this problem as a…
Training Variational Autoencoders with Buffered Stochastic Variational Inference
Rui Shu, Hung H. Bui, Jay Whang +1
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…
Fast Exploration with Simplified Models and Approximately Optimistic Planning in Model Based Reinforcement Learning
Ramtin Keramati, Jay Whang, Patrick Cho +1
Humans learn to play video games significantly faster than the state-of-the-art reinforcement learning (RL) algorithms. People seem to build simple models that are easy to learn to…