no code implementations • 20 Feb 2024 • Shivam Gupta, Ajil Jalal, Aditya Parulekar, Eric Price, Zhiyang Xun

Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$.

no code implementations • 13 Dec 2023 • Robin Netzorg, Ajil Jalal, Luna McNulty, Gopala Krishna Anumanchipalli

Perceptual modification of voice is an elusive goal.

1 code implementation • 5 Jun 2023 • Sriram Ravula, Brett Levac, Ajil Jalal, Jonathan I. Tamir, Alexandros G. Dimakis

Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction.

no code implementations • 26 Mar 2023 • Brett Levac, Ajil Jalal, Kannan Ramchandran, Jonathan I. Tamir

This leads to an improvement in image reconstruction fidelity over generative models that rely only on a marginal prior over the image contrast of interest.

1 code implementation • 1 Nov 2022 • Brett Levac, Sidharth Kumar, Ajil Jalal, Jonathan I. Tamir

In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models.

no code implementations • NeurIPS Workshop Deep_Invers 2021 • Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alex Dimakis, Jonathan Tamir

The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.

2 code implementations • NeurIPS 2021 • Ajil Jalal, Marius Arvinte, Giannis Daras, Eric Price, Alexandros G. Dimakis, Jonathan I. Tamir

The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.

1 code implementation • 23 Jun 2021 • Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann, Alexandros G. Dimakis, Eric Price

This motivates the introduction of definitions that allow algorithms to be \emph{oblivious} to the relevant groupings.

1 code implementation • 21 Jun 2021 • Ajil Jalal, Sushrut Karmalkar, Alexandros G. Dimakis, Eric Price

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 than, say, sparse vectors).

2 code implementations • 15 Feb 2021 • Giannis Daras, Joseph Dean, Ajil Jalal, Alexandros G. Dimakis

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models.

no code implementations • 23 Oct 2020 • Ajil Jalal, Sushrut Karmalkar, Alex Dimakis, Eric Price

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 than, say, sparse vectors).

no code implementations • 24 Jun 2020 • Eren Balevi, Akash Doshi, Ajil Jalal, Alexandros Dimakis, Jeffrey G. Andrews

This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.

1 code implementation • NeurIPS 2020 • Ajil Jalal, Liu Liu, Alexandros G. Dimakis, Constantine Caramanis

In analogy to classical compressed sensing, here we assume a generative model as a prior, that is, we assume the vector is represented by a deep generative model $G: \mathbb{R}^k \rightarrow \mathbb{R}^n$.

no code implementations • 12 May 2020 • Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett

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.

1 code implementation • NeurIPS 2019 • Qi Lei, Ajil Jalal, Inderjit S. Dhillon, Alexandros G. Dimakis

For generative models of arbitrary depth, we show that exact recovery is possible in polynomial time with high probability, if the layers are expanding and the weights are randomly selected.

1 code implementation • 17 Jun 2018 • Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, Alexandros G. Dimakis

We propose a novel method for compressed sensing recovery using untrained deep generative models.

1 code implementation • 26 Dec 2017 • Ajil Jalal, Andrew Ilyas, Constantinos Daskalakis, Alexandros G. Dimakis

Our formulation involves solving a min-max problem, where the min player sets the parameters of the classifier and the max player is running our attack, and is thus searching for adversarial examples in the {\em low-dimensional} input space of the spanner.

3 code implementations • ICML 2017 • Ashish Bora, Ajil Jalal, Eric Price, Alexandros G. Dimakis

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 the relevant domain.

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