Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neig…
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48 results for “spatial sparsity”
Deep neural network predicts event ticket prices considering spatial-temporal data sparsity.
problem Predicting future ticket prices from sparse and spatiotemporal data.
method Bi-level optimizing deep neural network with coarsening and refining layers, bi-level loss function.
result Our model outperforms other methods in real-world ticket price prediction.
New model predicts travel demand uncertainty with high accuracy.
problem Uncertainty and sparsity in sparse travel demand prediction.
method Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN).
result STZINB-GNN outperforms benchmarks in predicting travel demand uncertainty.
This paper explains a mechanism called phase collapse that improves image classification accuracy.
problem Understanding the role of non-linearities and convolutional filters in image classification.
method Demonstrates phase collapse as a mechanism that eliminates spatial variability and linearly separates classes.
result Phase collapse improves classification accuracy, while thresholding operators degrade performance.
Scalable psFA for fMRI data extracts sparse components.
problem Extracting neural representations from fMRI data with probabilistic formulation.
method Group level scalable probabilistic sparse factor analysis (psFA) with spatial sparsity, component pruning, and heteroscedastic noise modeling.
result Sparse components similar to group ICA and reduced noise in activated areas.
Predicts fine-grained OD matrices for ridesharing platforms to optimize supply-demand balance.
problem Accurately predicting spatial-temporal OD demands for ridesharing platforms.
method OD-CED model combining unsupervised space coarsening and encoder-decoder architecture.
result Significant improvement in prediction accuracy (45% RMSE reduction, 60% WAPE reduction).
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
Variational Autoencoders naturally become sparse in high-dimensional latent spaces, reducing overfitting risk.
problem Overfitting risk in high-dimensional latent spaces of Variational Autoencoders.
method Analyzing the natural sparsity phenomenon in VAEs, emphasizing its role in self-regularization and model capacity tuning.
result Sparsity in VAEs forces the model to focus on important features, reducing overfitting risk.
Spatial Adapter: Structured Spatial Decomposition and Closed-Form Covariance for Frozen Predictorsstat.ML
Spatial Adapter adds structured spatial representation to frozen predictors.
problem Efficiently adding spatial structure to pre-trained models.
method Structured spatial decomposition and closed-form covariance for residual fields.
result Adapter improves spatial prediction and uncertainty quantification.
Identification of regions of interest (ROI) associated with certain disease has a great impact on public health. Imposing sparsity of pixel values and extracting active regions simultaneously greatly complicate the image analysis. We address these challenges by introducing a novel region-selection penalty in the framew…
Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.
problem Estimating large spatio-temporal autoregressions with unknown spatial interactions.
method Sparse generalized Yule-Walker estimation, penalized regression, spatial and temporal dependence.
result Strong forecast improvements and evidence of spatial interactions in NO2 satellite data.
Kinetic Compressive Sensingphysics.med-ph
KCS improves parametric maps from PET images by reducing noise and variance.
problem Improving the quality of parametric maps from PET images due to noise.
method Kinetic Compressive Sensing (KCS) method based on a hierarchical Bayesian model and novel reconstruction algorithm.
result KCS produces spatially coherent images and parametric maps with lower noise and better contrast.
New method reduces dMRI scan times by achieving sparser representations.
problem Accelerate dMRI reconstruction while maintaining high resolution.
method Joint spatial-angular sparse coding with separable dictionaries.
result Significantly sparser representations of HARDI achieved.
A new neural network architecture reduces parameters by 94% while maintaining performance.
problem Reduction of trainable parameters in neural networks.
method Spatially-coupled sparse construction to allocate trainable parameters efficiently.
result Performance comparable to traditional neural networks with 94% fewer parameters.
New method recovers relative rates in spatial compositional data from IMS.
problem Challenges in analyzing spatial data from IMS due to competitive sampling.
method Hierarchical Variational Graph Fused Lasso using heavy-tailed graphical lasso prior and automatic differentiation variational inference.
result Our method outperforms state-of-the-practice point estimate methodologies in IMS and has superior posterior coverage.
Place Cells as Multi-Scale Position Embeddings: Random Walk Transition Kernels for Path Planningq-bio.NC
Model place cells as spatial embeddings for efficient path planning and cognitive map construction.
problem Encoding spatial navigation in the hippocampus.
method Model place cells using spectral decomposition of multi-step random walk transition kernels, inducing sparsity and adjacency.
result Place cells encode spatial information through non-negativity and inner-product structure, forming a cognitive map.
Modeling spatial extremes with non-Gaussian fields using SAR models and CNNs.
problem Challenges in modeling spatial data with heavy-tailed distributions and missing cells.
method Spatial autoregressive models with Generalized Extreme Value innovations, combined with CNN for fast parameter estimation.
result Effective modeling of spatial extremes in non-Gaussian fields, demonstrated on precipitation data.
Method selects significant spatial covariates in noisy data.
problem Identifying true spatial covariates in noisy data.
method Combines sparsity-promoting estimation with noise-robust model selection.
result Method reliably recovers true covariates under diverse noise scenarios.
Efficient spatio-temporal Gaussian process inference method.
problem Scalable Gaussian process inference for multivariate, spatio-temporal data.
method Combines spatio-temporal filtering with natural gradient variational inference, resulting in a scalable non-conjugate GP method.
result Linear scaling with respect to time and logarithmic scaling with respect to time steps.
Paper proposes a new method to handle spectral variability in hyperspectral unmixing.
problem Spectral variability within endmember classes affects unmixing performance.
method Adaptive bundles and double sparsity to promote sparsity on spectra and classes.
result Successfully determines variable number of classes and estimates their abundances.
New method reduces model selection sample complexity for geometric graphs.
problem Model selection in Gaussian Markov fields with sample deficiency.
method Introducing spatial stationarity to geometric graphs, developing information-theoretic bounds and efficient reconstruction techniques.
result Spatial stationarity leads to significant reduction in sample complexity for consistent recovery.
Method uses JPEG transform for faster image classification.
problem Efficient image classification with compressed data.
method Reformulates residual networks for JPEG compressed images.
result Mathematically equivalent to spatial domain networks up to ReLu approximation.
Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lassostat.ML
New method controls false detections in brain activity localization.
problem Statistical control of false detections in brain activity localization.
method Adapted Lasso estimator for spatio-temporal MEG/EEG data.
result Offers statistical guarantees and adaptive method for thresholding.
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.
Efficiently estimates covariance for sparse functional data.
problem Sparse data in functional analysis.
method Random-knots and B-spline estimators for covariance function.
result Asymptotic pointwise covariance estimates for sparsified data.
Regularized variants of Principal Components Analysis, especially Sparse PCA and Functional PCA, are among the most useful tools for the analysis of complex high-dimensional data. Many examples of massive data, have both sparse and functional (smooth) aspects and may benefit from a regularization scheme that can captur…
New neural operators learn structured patterns efficiently.
problem Learning and representing complex, structured patterns in data.
method Sparse autoencoder neural operators (SAE-NOs) parameterize concepts as functions, enabling efficient and structured representation.
result SAE-FNOs learn localized patterns and generalize across different scales and discretizations.
Dynamic models learn from sparse, interacting sub-systems.
problem Learning robust models for systems with local views and spatial locations.
method Abstracting the system as a collection of sparsely interacting sub-systems, each with a learned topology informed by spatial structure.
result Models are more robust to the number of available views and generalize better to novel tasks.
Flexible Cox model for time-dependent covariates with complex sparsity patterns.
problem Lack of flexibility in enforcing specific sparsity patterns in time-dependent Cox models.
method Proposes a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
result Achieves accurate estimation with low false alarm rates for complex covariate structures.
Robust STAP with coprime arrays reduces clutter using sparse modeling.
problem Limited performance due to training samples support in practical applications.
method Two-stage approach: 1) RD virtual snapshot, 2) RD sparse measurement modeling with OMP-like recovery.
result Robust to prior knowledge errors, good clutter suppression performance.
New method combines prior knowledge and brain atlases for fMRI analysis.
problem Matrix factorization formulation of task-related fMRI problem.
method Incorporates prior knowledge from experimental design and brain atlases, uses novel sparsity promoting constraint.
result Efficiently copes with uncertainties and selection of sparsity parameters.
Sparse-mode DMD disambiguates local and global modes in spatiotemporal data.
problem Disambiguating local and global modes in spatiotemporal data.
method Sparse-mode DMD with sparsity-promoting regularization.
result Explicitly constructs discrete and continuous spectra.
Develops a new attack model to better capture structural information in adversarial examples.
problem Lp norm-based adversarial attacks fail to capture structural information in input images.
method Structured Adversarial Attack (StrAttack) using ADMM framework to achieve strong group sparsity.
result StrAttack achieves strong group sparsity in adversarial perturbations with similar Lp norm distortion.
A neural network improves DOA estimation from a single snapshot.
problem Estimating DOAs from a single snapshot with limited aperture.
method Deep learning architecture trained to generate high-resolution spatial spectrum.
result Our (SP)-Net outperforms classical methods.
Proposes a new regularizer for multi-task regression using Wasserstein geometry.
problem Lack of spatial information in multi-task regression models.
method Wasserstein geometry for multi-task regression, using unbalanced optimal transport.
result Improved statistical power and flexibility in multi-task regression models.
Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines. Yet, scalable inference remains computa- tionally challenging both due to the high resolution modelling generally required and the analytically intractable likelihood function. Here, we expl…
SRHM explains deep learning's hierarchy and insensitivity to transformations.
problem Understanding how deep networks learn hierarchical and invariant representations.
method Introducing sparsity to generative hierarchical models of data.
result Hierarchical representations and insensitivity to transformations correlate strongly with deep network performance.
Understanding how housing values evolve over time is important to policy makers, consumers and real estate professionals. Existing methods for constructing housing indices are computed at a coarse spatial granularity, such as metropolitan regions, which can mask or distort price dynamics apparent in local markets, such…
Predictive models can be used on high-dimensional brain images for diagnosis of a clinical condition. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to …
SpINNEr uses matrix regression to analyze brain connectivity, improving accuracy over other methods.
problem Analyzing multi-dimensional data like brain imaging arrays using traditional scalar regression methods.
method SpINNEr applies matrix regression with nuclear norm and lasso norms to encourage low rank and sparse solutions.
result SpINNEr outperforms other methods in estimating brain connectivity, especially in well-connected regions.
Inverse inference, or "brain reading", is a recent paradigm for analyzing functional magnetic resonance imaging (fMRI) data, based on pattern recognition and statistical learning. By predicting some cognitive variables related to brain activation maps, this approach aims at decoding brain activity. Inverse inference ta…
A new stochastic solver improves Convolutional Sparse Coding efficiency.
problem Efficiency and sparsity in Convolutional Sparse Coding.
method Randomized subsampling strategy in spatial domain for online learning.
result Improved execution time with no loss in learning quality.
A neuromemristive HTM architecture boosts robustness and performance.
problem Improving robustness and performance of HTM algorithms.
method Developed a neuromemristive crossbar architecture for HTM, incorporating memristors and neurogenesis.
result Enhanced robustness and performance of HTM through neuromemristive architecture.
A new method matches measures across different spaces using cost-regularized optimal transport.
problem Matching measures in different spaces without aligned data.
method Cost-regularized optimal transport formulation to match measures across two Euclidean spaces.
result Demonstrated applicability to single-cell spatial transcriptomics/multiomics matching tasks.
Structured and sparse partial least squares coherence for multivariate cortico-muscular analysisstat.AP
New algorithm extracts shared latent space for cortico-muscular interactions.
problem Challenges of high dimensionality and limited sample sizes in multivariate cortico-muscular analysis.
method Structured and sparse partial least squares coherence (ssPLSC) algorithm.
result ssPLSC achieves competitive or better performance in scenarios with limited sample sizes and high noise levels.
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approachesphysics.data-an
Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution ena…
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BO…
The iterative reweighted Mixed-Norm Estimate for spatio-temporal MEG/EEG source reconstructionstat.AP
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatia…