We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of -sparse signals b…
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48 results for “Sequential Compressive Sensing”
We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean…
Interpretable RNN uses sparse recovery for better performance.
problem Interpreting the internal workings of RNNs.
method Sequential Sparse Recovery + SISTA algorithm.
result SISTA-RNN achieves better performance and is more interpretable.
Compressed sensing improves MRI scans with data-driven learning.
problem Challenges in applying compressed sensing from research to clinical practice.
method Data-driven learning to address challenges of hand-crafted priors, tuning parameters, and long reconstruction times.
result Compressed sensing can have greater clinical impact with data-driven learning.
A new model for context-aware recommendations using LSTM and latent context.
problem Challenges in incorporating context into recommendation models, especially sparsity and dimensionality issues.
method Sequential latent context modeling using LSTM, reducing context dimensions to a compressed latent space.
result The proposed SLCM outperforms state-of-the-art CARS models in empirical analysis.
New RNN reconstructs video frames from sparse measurements.
problem Sequential signal reconstruction from compressive measurements.
method Unfolding proximal gradient method for l1-l1 minimization.
result Outperforms state-of-the-art RNN models in video frame reconstruction.
Review of image compressive sensing algorithms for beginners.
problem Efficiently processing images with limited data.
method Comprehensive review of Total variation methods and other algorithms.
result Standardized comparison of algorithms for compressive sensing applications.
This paper proposes a simple adaptive sensing and group testing algorithm for sparse signal recovery. The algorithm, termed Compressive Adaptive Sense and Search (CASS), is shown to be near-optimal in that it succeeds at the lowest possible signal-to-noise-ratio (SNR) levels, improving on previous work in adaptive comp…
Autoencoders learn compressed representations via mutual information maximization.
problem Learning efficient compressed representations of high-dimensional data.
method Proposes Uncertainty Autoencoders that treat latent representations as noisy projections and optimize mutual information.
result 32% improvement in statistical compressed sensing of high-dimensional datasets.
Deep learning produces efficient ternary projections for image compression.
problem Efficiently compress and reconstruct sparse signals from incomplete measurements.
method End-to-end deep learning architecture for learning projection matrices and reconstruction operators.
result Deep learning approach yields more efficient ternary projections compared to state-of-the-art methods.
Convolutional factor analysis improves compressive sensing with fewer measurements.
problem Efficiently reconstructing images from limited compressed measurements.
method Learn convolutional dictionaries from compressed measurements using ADMM.
result Achieves comparable classification accuracy with 30% fewer measurements.
Bayesian compressive sensing speeds up object detection in video sequences.
problem Efficiently detecting objects in large video datasets.
method Bayesian compressive sensing methods for object detection.
result Bayesian methods achieve similar or better accuracy than greedy algorithms but faster.
Study 1-bit compressive sensing with generative models, improving recovery accuracy.
problem Accurately recover sparse vectors from binary measurements with generative models.
method Analyzes noiseless and noisy 1-bit measurements with i.i.d.~Gaussian and Lipschitz continuous generative priors, proving sample complexity bounds and stability properties.
result Proves sample complexity bounds and stability properties for 1-bit compressive sensing with generative models.
As a lossy compression framework, compressed sensing has drawn much attention in wireless telemonitoring of biosignals due to its ability to reduce energy consumption and make possible the design of low-power devices. However, the non-sparseness of biosignals presents a major challenge to compressed sensing. This study…
Sparse-Gen uses generative models to improve compressed sensing with full signal recovery.
problem Recovering signals with fewer measurements than traditional methods allow.
method Sparse-Gen framework that allows for sparse deviations from the support set.
result Achieves full signal recovery over the full space of signals, not just the support.
Robust 1-bit Compressive Sensing with Partial Gaussian Circulant Matrices and Generative Priorscs.LG
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
problem Accurately recovering vectors from 1-bit measurements using structured matrices.
method Correlation-based optimization with randomly signed partial Gaussian circulant matrices and generative models.
result Recovery guarantees match those for i.i.d. Gaussian matrices but with faster computation.
Sharp asymptotics derived for phase retrieval and compressed sensing with random generative priors.
problem Phase retrieval and compressed sensing with random measurement matrices.
method Sharp asymptotics derived for optimal performance and polynomial algorithm for random generative priors.
result Compressed phase retrieval becomes tractable with random generative priors, unlike sparse priors.
New algorithm speeds up cluster-based compressive sensing tasks.
problem Efficiently solving multiple compressive sensing tasks with shared information.
method Combines Monte Carlo sampling with iterative linear solvers to avoid explicit covariance matrix computation.
result Up to thousands of times faster and orders of magnitude more memory-efficient compared to existing methods.
This letter proposes a sparse diffusion steepest-descent algorithm for one bit compressed sensing in wireless sensor networks. The approach exploits the diffusion strategy from distributed learning in the one bit compressed sensing framework. To estimate a common sparse vector cooperatively from only the sign of measur…
Proposes using equivariant generative models for compressed sensing with unknown orientations.
problem Recovering signals with unknown orientations from underdetermined systems of linear measurements.
method Equivariant variational autoencoder as a generative prior for compressed sensing.
result Signals with unknown orientations can be recovered using iterative gradient descent on the latent space of equivariant models.
A method to optimize deep networks by sequentially minimizing risk functions.
problem Optimizing deep networks during training to find global optima.
method Surfing: Iterative optimization over incrementally trained deep networks.
result The method can find global optima and improve compressed sensing performance.
Diffusion Generative Models Meet Compressed Sensing, with Applications to Imaging and Financestat.ML
CSDM integrates compressed sensing into diffusion models for faster data generation.
problem Efficiently generating synthetic data in high-dimensional spaces.
method Integrating compressed sensing into diffusion models (CSDM) to reduce dimensionality and accelerate inference.
result Achieves provably faster convergence and better latent space dimension selection.
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
New method uses generative priors for compressive sensing with sparse solutions.
problem Fundamental linear inverse problem in compressive sensing.
method Sparse Bayesian learning with conditional Gaussianity.
result Ability to learn from few compressed and noisy samples without optimization.
Improves convergence speed in compressive sensing with a new probabilistic approach.
problem Efficiently solving the best subset selection problem in compressive sensing.
method Smooth probabilistic reformulation of regularized regression.
result Empirically outperforms existing compressive sensing algorithms across various settings.
The paper provides theoretical guarantees for optimized sampling in compressed sensing, showing error vanishes with more measurements.
problem Theoretical and practical improvements in compressed sensing with optimized sampling schemes.
method Theoretical analysis and empirical experiments with optimized sampling schemes for subsampled unitary matrices.
result The error caused by measurement noise vanishes with an increasing number of measurements for optimized sampling schemes, assuming Gaussian noise.
Paper improves compressed sensing with prior probability information.
problem Enhancing compressed sensing accuracy with prior information.
method Designing a sensing matrix and sparse recovery algorithm using probability-based prior information.
result Proposed methods outperform existing CS systems in simulations.
Optimized sampling scheme for compressed sensing combining randomness and determinism.
problem Improving compressed sensing performance with deterministic sampling.
method Optimized sampling scheme combining random and deterministic selection of rows.
result Measurable improvements in image compressed sensing for generative and sparse priors.
End-to-end deep generative model for video compression.
problem Efficiently compressing video data with deep learning.
method Variational autoencoder (VAE) for sequential data, combined with neural image compression techniques.
result Our model achieves competitive rate-distortion results on diverse video content.
We consider the problem of robust compressed sensing whose objective is to recover a high-dimensional sparse signal from compressed measurements corrupted by outliers. A new sparse Bayesian learning method is developed for robust compressed sensing. The basic idea of the proposed method is to identify and remove the ou…
This letter proposes a dictionary learning algorithm for blind one bit compressed sensing. In the blind one bit compressed sensing framework, the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. In this context, the multiplication of measurement matrix $\Ab$ an…
Paper improves calcium signal deconvolution using efficient state-space models.
problem Deconvolving calcium signals from imaging data.
method Dynamic compressed sensing framework with two nested EM algorithms.
result Proves recovery guarantees and derives confidence bounds for state estimates.
VQ-DRAW compresses images and generates realistic samples.
problem Learning compact discrete representations of images.
method Sequential discrete VAE with vector quantization.
result VQ-DRAW effectively compresses and generates images.
Generative Adversarial Networks improve compressed sensing for task-specific reconstruction.
problem Improving compressed sensing for specific tasks using neural networks.
method Task-aware training of Generative Adversarial Networks (GANs) to impose structure in compressed sensing problems.
result GANs can generate input features for general inference tasks and improve reconstruction and classification performance.
A hybrid framework reduces ML complexity on edge devices.
problem Limited memory and energy on edge devices.
method Compressed data collection and tailored deep learning network.
result Significant reduction in computational complexity and memory.
AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.
problem Binary classification in robust one-bit compressed sensing with adversarial errors.
method AdaBoost and max--margin-classifier approach, with convergence rates improved under certain feature conditions.
result Improved convergence rates and explanation for harmless interpolating adversarial noise.
Improved image reconstruction from sparse measurements using generative models.
problem Signal recovery from limited compressed measurements.
method Generative model with constrained latent variables for stable signal reconstruction.
result Improved reconstruction accuracy and preservation of realistic features.
Paper proposes robust compressed sensing using generative models.
problem Estimating high-dimensional vectors from noisy linear equations with heavy-tailed or outlier data.
method Inspired by Median-of-Means (MOM), proposes an algorithm for robust recovery.
result Guarantees recovery for heavy-tailed data, even in the presence of outliers.
New algorithm improves polynomial chaos approximations using compressive sensing.
problem Improving the efficiency and accuracy of polynomial chaos expansions.
method Develops a two-step optimization procedure combining compressive sensing with basis adaptation.
result Optimal sparsity in polynomial chaos approximations with reduced dimensionality.
A new deep learning framework for efficient IoT data compression and inference.
problem Limited bandwidth and power resources in IoT sensors.
method Co-designed deep learning framework that maximizes sensing goal accuracy.
result Superior performance compared to benchmark models.
We improve existing results in the field of compressed sensing and matrix completion when sampled data may be grossly corrupted. We introduce three new theorems. 1) In compressed sensing, we show that if the m \times n sensing matrix has independent Gaussian entries, then one can recover a sparse signal x exactly by tr…
New coherence parameter for GNNs with Fourier measurements improves signal recovery.
problem Characterizing generative compressed sensing with Fourier measurements.
method Subspace counting arguments and high-dimensional probability theory.
result First known restricted isometry guarantee for generative compressed sensing with subsampled isometries.
Paper uses SGLD to recover signals from generative models, proving convergence under mild conditions.
problem Signal recovery from generative priors in compressed sensing.
method Stochastic Gradient Langevin Dynamics (SGLD) for signal recovery.
result SGLD converges to the true signal under mild assumptions on the generative model.
Cover trees speed up MRI fingerprint recovery by reducing computation.
problem Efficiently reconstructing MRI fingerprint signals from compressed sensing data.
method Use cover trees for fast approximate nearest neighbor searches in IPG algorithm.
result Achieves 2-3 orders of magnitude reduction in computations.
Unified approach for robust low rank matrix estimation with adversaries.
problem Robust low rank matrix estimation in the presence of adversaries.
method Unified approach combining Huber loss and nuclear norm penalization.
result Sharp estimation error bounds for matrix compressed sensing and completion.
A new method for matching binary distributions using compressed sensing.
problem Matching fixed-length binary distributions efficiently.
method Inspired by compressed sensing, the paper introduces sparsity in binary sources via position modulation and a simple exact matcher based on Gaussian signal quantization. The dematcher uses GAMP for low-complexity dematching.
result The proposed method achieves asymptotically optimal performance, with vanishing reconstruction error in a proper limit.
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.
problem Recovering sparse signals from 1-bit compressed measurements.
method Binary Iterative Hard Thresholding (BIHT) algorithm.
result BIHT converges with only O(k/ε) measurements, optimal for recovery.
Improved compressed sensing using a generator that learns from measurements.
problem Signal recovery accuracy in compressed sensing.
method Proposes a framework that uses measurement-conditional generative models to refine signal estimation.
result Uniformly superior performance with up to an order of magnitude reduction in reconstruction error.