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09/16/2023
Solving Quadratic Systems with Full-Rank Matrices Using Sparse or Generative Priors
The problem of recovering a signal x∈ℝ^n from a quadratic system {y_i=x^...
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04/13/2023
DiffFit: Unlocking Transferability of Large Diffusion Models via Simple Parameter-Efficient Fine-Tuning
Diffusion models have proven to be highly effective in generating high-q...
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03/30/2023
DDP: Diffusion Model for Dense Visual Prediction
We propose a simple, efficient, yet powerful framework for dense visual ...
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10/11/2022
Misspecified Phase Retrieval with Generative Priors
In this paper, we study phase retrieval under model misspecification and...
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09/21/2022
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
In this paper, we propose projected gradient descent (PGD) algorithms fo...
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05/31/2022
Non-Iterative Recovery from Nonlinear Observations using Generative Models
In this paper, we aim to estimate the direction of an underlying signal ...
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03/18/2022
Generative Principal Component Analysis
In this paper, we study the problem of principal component analysis with...
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08/08/2021
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priors
In 1-bit compressive sensing, each measurement is quantized to a single ...
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06/29/2021
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Compressive phase retrieval is a popular variant of the standard compres...
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06/22/2020
The Generalized Lasso with Nonlinear Observations and Generative Priors
In this paper, we study the problem of signal estimation from noisy non-...
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02/05/2020
Sample Complexity Bounds for 1-bit Compressive Sensing and Binary Stable Embeddings with Generative Priors
The goal of standard 1-bit compressive sensing is to accurately recover ...
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08/28/2019
Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models
The goal of standard compressive sensing is to estimate an unknown vecto...
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10/23/2018
Model Selection for Nonnegative Matrix Factorization by Support Union Recovery
Nonnegative matrix factorization (NMF) has been widely used in machine l...
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03/30/2017
The Informativeness of k-Means and Dimensionality Reduction for Learning Mixture Models
The learning of mixture models can be viewed as a clustering problem. In...
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12/27/2016