arXiv research

New filter bank sparsifying transforms outperform patch-based methods for image denoising.

Two-layer model sparsifies image residuals for CT image reconstruction.

A new model improves CT image quality from low-dose scans.

Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary. Synthesis sparsifying dictionaries that are directly adapted to data have been popular in applications such as image denoising, inpainting, and medical image reconstruction. In this work, we focus in…

New multi-layer transform models improve image denoising.

Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT. This property has been heavily exploited in various applications in image processing and medical imaging. Compressed sensing exploits the sparsity of images or image patches in a transform domain or synth…

Study improves epidemic forecasting with a sparsified GSRNN.

New algorithm improves convergence of dictionary learning models.

Compressed sensing is a powerful tool in applications such as magnetic resonance imaging (MRI). It enables accurate recovery of images from highly undersampled measurements by exploiting the sparsity of the images or image patches in a transform domain or dictionary. In this work, we focus on blind compressed sensing (…

In this paper, we consider a privacy preserving encoding framework for identification applications covering biometrics, physical object security and the Internet of Things (IoT). The proposed framework is based on a sparsifying transform, which consists of a trained linear map, an element-wise nonlinearity, and privacy…

New method preserves spectral clustering performance under aggressive sparsification and quantization.

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images. We propose a new method for CT reconstruction that combines penalized weighted-least squares reconstruction (PWLS) with regularization based on a sparsifying…

DECT-MULTRA improves material decomposition in CT images.

Sparse representations using data dictionaries provide an efficient model particularly for signals that do not enjoy alternate analytic sparsifying transformations. However, solving inverse problems with sparsifying dictionaries can be computationally expensive, especially when the dictionary under consideration has a …

Forecaster uses graph Transformers to forecast spatial and time-dependent data.

Bayesian approach sparsifies neural networks efficiently.

Sparse Transformers can approximate dense Transformers with only O(n) connections.

So-called sparse estimators arise in the context of model fitting, when one a priori assumes that only a few (unknown) model parameters deviate from zero. Sparsity constraints can be useful when the estimation problem is under-determined, i.e. when number of model parameters is much higher than the number of data point…

Efficiently estimates covariance for sparse functional data.

Bayesian method sparsifies gated RNNs, improving speed and interpretability.

Fine-tuning normalization layers can reconstruct smaller networks.

We sparsify gated RNNs by simplifying their structure.

A new method reduces memory requirements for Graph Transformers by sparsely training a network.

SGMM clusters data in one pass, reducing dimensionality.

Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data. The dictionary learning problem is non-convex, and usually solved via computationally complex iterative algorithms. Furthermore, the resulting transforms obtained generally lack structure…

Enhances DSN with multi-family wavelet transforms and sparsity.

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $Ω(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The funda…

New method uses random projections to estimate densities and modes efficiently.

Newton-LESS sparsifies Gaussian sketching for faster optimization.

Faster LSTM training with structured sparsity.

Sparsifying data reduces adversarial perturbation effects in deep networks.

This paper speeds up kernel methods using sparsified Gaussian sketches.

Estimates mean of distributed vectors with sparsification and spatial/temporal correlations.

The paper decouples shrinkage and selection in Bayesian Quantile Regression.

Proposes sparsified intervals for high-dimensional regression coefficients.

IFR-Net improves MRI images with faster, better detail recovery.

We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent …

A new method sparsifies neural networks by reducing sensitive parameters to zero.

The paper sparsifies networks by finding efficient paths in their functional space.

Sparse representations help protect neural networks from adversarial attacks.

New method sparsifies hybrid neural ODEs for better performance and stability.

Explains neural network properties with a simple operation.

ATOMO reduces communication in distributed learning by sparsifying gradients.

We consider the problem of estimating the support of a vector $β^* \in \mathbb{R}^{p}$ based on observations contaminated by noise. A significant body of work has studied behavior of $\ell_1$-relaxations when applied to measurement matrices drawn from standard dense ensembles (e.g., Gaussian, Bernoulli). In this paper,…

We derive a new proof to show that the incremental resparsification algorithm proposed by Kelner and Levin (2013) produces a spectral sparsifier in high probability. We rigorously take into account the dependencies across subsequent resparsifications using martingale inequalities, fixing a flaw in the original analysis…

This paper considers mean-variance optimization under uncertainty, specifically when one desires a sparsified set of optimal portfolio weights. From the standpoint of a Bayesian investor, our approach produces a small portfolio from many potential assets while acknowledging uncertainty in asset returns and parameter es…

The paper develops a method to sparsify magnetic Laplacians using multi-type spanning forests.

New $L_1$ regularization controls neural network generalization error and sparsifies input dimensions.