289 citations · 656 across the 17 of their papers we have counts for
18 papers
Double Blind Imaging with Generative Modeling
Brett Levac, Ajil Jalal, Kannan Ramchandran +1
Blind inverse problems in imaging arise from uncertainties in the system used to collect (noisy) measurements of images. Recovering clean images from these measurements typically r…
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar +2
Diffusion models are a remarkably effective way of learning and sampling from a distribution . In posterior sampling, one is also given a measurement model and…
PerMod: Perceptually Grounded Voice Modification with Latent Diffusion Models
Robin Netzorg, Ajil Jalal, Luna McNulty +1
Perceptual modification of voice is an elusive goal. While non-experts can modify an image or sentence perceptually with available tools, it is not clear how to similarly modify sp…
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Sriram Ravula, Brett Levac, Yamin Arefeen +3
Magnetic resonance imaging (MRI) is a powerful medical imaging modality, but long acquisition times limit throughput, patient comfort, and clinical accessibility. Diffusion-based g…
MRI Reconstruction with Side Information using Diffusion Models
Brett Levac, Ajil Jalal, Kannan Ramchandran +1
Magnetic resonance imaging (MRI) exam protocols consist of multiple contrast-weighted images of the same anatomy to emphasize different tissue properties. Due to the long acquisiti…
Accelerated Motion Correction with Deep Generative Diffusion Models
Brett Levac, Sidharth Kumar, Ajil Jalal +1
Magnetic Resonance Imaging (MRI) is a powerful medical imaging modality, but unfortunately suffers from long scan times which, aside from increasing operational costs, can lead to…
Robust Compressed Sensing MRI with Deep Generative Priors
Ajil Jalal, Marius Arvinte, Giannis Daras +3
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems. However, to date this framework has been…
Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann +2
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification…
Instance-Optimal Compressed Sensing via Posterior Sampling
Ajil Jalal, Sushrut Karmalkar, Alexandros G. Dimakis +1
We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather th…
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Giannis Daras, Joseph Dean, Ajil Jalal +1
We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models. Instead of optimizing only over the initi…
High Dimensional Channel Estimation Using Deep Generative Networks
Eren Balevi, Akash Doshi, Ajil Jalal +2
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network. Channel estimatio…
Robust Compressed Sensing using Generative Models
Ajil Jalal, Liu Liu, Alexandros G. Dimakis +1
The goal of compressed sensing is to estimate a high dimensional vector from an underdetermined system of noisy linear equations. In analogy to classical compressed sensing, here w…
Deep Learning Techniques for Inverse Problems in Imaging
Gregory Ongie, Ajil Jalal, Christopher A. Metzler +3
Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central pre…
Inverting Deep Generative models, One layer at a time
Qi Lei, Ajil Jalal, Inderjit S. Dhillon +1
We study the problem of inverting a deep generative model with ReLU activations. Inversion corresponds to finding a latent code vector that explains observed measurements as much a…
Compressed Sensing with Deep Image Prior and Learned Regularization
Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi +3
We propose a novel method for compressed sensing recovery using untrained deep generative models. Our method is based on the recently proposed Deep Image Prior (DIP), wherein the c…
The Robust Manifold Defense: Adversarial Training using Generative Models
Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis +1
We propose a new type of attack for finding adversarial examples for image classifiers. Our method exploits spanners, i.e. deep neural networks whose input space is low-dimensional…
Compressed Sensing using Generative Models
Ashish Bora, Ajil Jalal, Eric Price +1
The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in…
Online Budgeted Repeated Matching
Ajil Jalal, Rahul Vaze, Umang Bhaskar
A basic combinatorial online resource allocation problem is considered, where multiple servers have individual capacity constraints, and at each time slot, a set of jobs arrives, t…