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 “region sparsity”
Study on the complexity of 1D ReLU neural networks, proving growth in linear regions.
problem Understanding the complexity and expressivity of 1D ReLU neural networks.
method Analyzing the number of linear regions in randomly initialized, fully connected 1D ReLU networks in the infinite-width limit.
result The expected number of linear regions grows as a function of the number of neurons in each layer.
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…
Multi-Region Neural Representation: A novel model for decoding visual stimuli in human brainsstat.ML
Novel model for decoding visual stimuli in human brains.
problem Challenges in MVP techniques, including noise and sparsity, and the cost of brain studies.
method Automatic detection of active regions, new Gaussian smoothing method, combining fMRI data sets.
result Superior performance compared to state-of-the-art methods.
Proposes methods for constructing confidence sets in high-dimensional structured sparsity.
problem Building confidence regions for high-dimensional structured sparsity models.
method Desparsification of the estimator, structured matrix norm penalty, and asymptotic pivot construction.
result Developed methods for constructing asymptotic confidence regions in high-dimensional structured sparsity models.
RbX generates local explanations for black-box models using greedy polytope construction.
problem Generating interpretable local explanations for complex models.
method Greedy algorithm for building a convex polytope to approximate feature regions.
result RbX can more accurately identify important features compared to existing methods.
New model tackles region-sparse regression with hierarchical Gaussian process.
problem Sparse and dependent parameter vectors in regression settings.
method Hierarchical model with transformed Gaussian process and structured Fourier coefficients.
result Substantial improvements over comparable methods in simulated and real datasets.
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be inferred from fluorescence measurements obtained by binding a complement…
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…
Superhighway bypasses data sparsity in cross-domain CF.
problem Data sparsity in cross-domain collaborative filtering.
method Explicit relation-enrichment procedure to enhance cross-domain connectivity.
result Significantly improves recommendation performance in both target and source domains.
Developed a multiway classification method for sparse data.
problem Classification of multiway arrays with sparsity.
method Extended Distance Weighted Discrimination (DWD) to multiway context, accounting for sparsity.
result Improves classification accuracy in multiway structured data.
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.
DO-IQS: Dynamics-Aware Offline Inverse Q-Learning for Optimal Stopping with Unknown Gain Functionsstat.ML
DO-IQS recovers optimal stopping region from expert trajectories, addressing specific challenges.
problem Recovering optimal stopping region from expert trajectories with unknown gain functions.
method Dynamics-Aware Offline Inverse Q-Learning incorporating temporal information and confidence-based oversampling.
result Demonstrated performance on real and artificial data, including optimal intervention for critical events.
Partition-wise Linear Modelsstat.ML
Region-specific linear models are widely used in practical applications because of their non-linear but highly interpretable model representations. One of the key challenges in their use is non-convexity in simultaneous optimization of regions and region-specific models. This paper proposes novel convex region-specific…
Improved fMRI analysis models enhance classification performance and select relevant brain regions.
problem Inaccurate selection of relevant brain components in MVPA models.
method Hybrid Sparsity-Ranked LASSO (JSRL) method integrating component-level and voxel-level activity.
result JSRL models achieve up to 51.7% improvement in cross-validated deviance and 7.3% improvement in cross-validated AUC.
This work extends neural networks to automatically select features by stochastically penalizing feature involvement.
problem Feature selection in machine learning models.
method Stochastic regularization to select features instead of layer weights.
result Superior efficiency compared to classical methods with minimal computational overhead.
A new pruning method finds sparse minimizers in flat regions of deep neural networks.
problem Finding sparse minimizers in flat regions of deep neural networks.
method Directional pruning using an proximal gradient algorithm.
result Empirical results show promising sparsity (92%) and comparable minima to SGD.
The paper provides uniform inference for high-dimensional graphical models.
problem Estimating dependencies in large sets of variables with high-dimensional data.
method Uniform estimation rates and sparsity guarantees for the square-root estimator in random design under approximate sparsity conditions.
result The paper establishes uniform estimation rates and sparsity guarantees for graphical models in high-dimensional settings.
We study a generalized framework for structured sparsity. It extends the well-known methods of Lasso and Group Lasso by incorporating additional constraints on the variables as part of a convex optimization problem. This framework provides a straightforward way of favouring prescribed sparsity patterns, such as orderin…
Unified algorithms for structured sparsity problems with optimal convergence rates.
problem Solving minimization problems with structured sparsity assumptions.
method Unified continuum of preconditioned forward-backward operator splitting algorithms and accelerated algorithms.
result The continuum of algorithms attains the theoretically optimal rate of convergence.
New algorithm recovers sparse signals from linearly sparse dictionaries efficiently.
problem Recovering sparse signals from linearly sparse dictionaries.
method Spectral method on reweighted covariance matrices.
result First polynomial-time algorithm for near-linear sparsity in overcomplete dictionaries.
BAND tackles high-dimensional distribution estimation with sparse Bayesian networks.
problem High-dimensional distribution estimation suffers from the curse of dimensionality.
method Sparse Bayesian network approach with sparsity-aware conditional mean methods.
result Achieves polynomial total variation convergence rates in high dimensions.
Local Coverage Governs Memorization in Diffusion Modelscond-mat.dis-nn
Local data coverage governs memorization in diffusion models.
problem Memorization in diffusion models
method Derive a theoretical criterion based on local data coverage
result Predicts memorization based on density of training data in neighborhood and dataset size
High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be \emph{safe}. In this …
Proposes sparse local and regional counterfactual rules for robust recourses.
problem Challenges in counterfactual explanations, especially stability, synthesis, and implementation.
method Probabilistic framework using Random Forest to derive sparse local and regional counterfactual rules.
result Effective recourses derived from high-density regions, providing sparse and robust counterfactual rules.
New screening test for LASSO reduces complexity.
problem Efficient screening for LASSO problems.
method Joint screening test for LASSO problem, applied to sphere and dome regions.
result Effective screening of atoms reduces computational complexity.
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.
Study confirms sparse coding in whole brain using MRI data.
problem Sparse coding in the whole brain's neural activities.
method Applied various matrix factorization methods to fMRI data.
result Sparse coding hypothesis in information representation in the whole human brain is confirmed.
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).
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…
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivitystat.AP
New model infers brain connectivity using nonlinear SVARMs.
problem Inferring effective brain network connectivity.
method Kernel-based nonlinear structural vector autoregressive models (SVARMs).
result Unveils previously unknown causal links between brain regions.
A new greedy method tackles sparse coding for better image processing.
problem Imbalanced sparsity in and norms for image processing.
method Greedy matching pursuit for norm optimization.
result Efficient method for sparse coding and dictionary learning.
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.
Study of spiked matrix models with generative priors.
problem Performance enhancement in signal processing and statistical inference.
method Analysis of spiked matrix models with generative priors, using Bayes-optimal performance and enhanced spectral algorithms.
result Approximate message passing algorithm reaches optimal performance.
Study on detecting a single spike in high-dimensional data matrices.
problem Detecting a single unknown spike in high-dimensional rectangular data matrices.
method Analysis of likelihood ratio between spiked and null models, using Gaussian fluctuations and Talagrand's interpretation of cavity method.
result Asymptotic Gaussian fluctuations of the likelihood ratio below the BBP threshold, with open maximal parameter region.
Proposes MOCE method for simultaneous inference in high-dimensional linear models.
problem Simultaneous inference in high-dimensional linear models with model selection.
method Method of contraction and expansion (MOCE) for debiasing estimation.
result Established theoretical guarantees for MOCE procedure and simultaneous confidence regions.
Post-shifted BN prevents filter collapse in BN networks, improving model performance.
problem Filter collapse in BN networks reduces network capacity and harms model performance.
method Post-shifted BN (psBN) to prevent filter collapse by making BN parameters trainable again.
result psBN prevents filter collapse and increases model performance in various tasks.
New method classifies brain networks for distinguishing disorders.
problem Classifying brain networks for various disorders.
method Uses edge weights and network structure via penalties promoting sparsity.
result Effective in distinguishing brain disorders from fMRI data.
This paper tackles sparse blind deconvolution with short signals and demonstrates recovery of near ground truth kernels.
problem Recovering two unknown signals from their convolution, especially when one is short and sparsely supported.
method Formulated as a nonconvex optimization problem over the sphere, using a descent algorithm that escapes strict saddle points.
result Near shift truncation of the ground truth kernel can be recovered under specific conditions.
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functionscs.LG
Simplified screening tests for data points in optimization.
problem Discarding irrelevant data points in empirical risk minimization.
method Designing loss functions and regularizing convex losses to induce sparsity, using ellipsoidal approximations.
result Automatic discarding of data samples without losing optimization guarantees.
Channel gating reduces CNN computation cost by skipping ineffective feature regions.
problem Reducing computation cost in CNNs while maintaining accuracy.
method Dynamic, fine-grained pruning scheme that identifies and skips computation on ineffective feature regions.
result 2.7-8.0x reduction in FLOPs and 2.0-4.4x reduction in memory accesses with minimal accuracy loss.
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.
Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, the interpretability of PCA remains limited. However, in neuroimaging, it is essential to uncove…
New framework infers sparse inter-subject connections from dense intra-data.
problem Inferring sparse inter-subject connections from dense intra-data in neuroscience.
method Gaussian graphical models, alternative parameter estimation, and chord procedure for inference.
result Asymptotic consistency of estimator and inference method without sparsity assumption.
In plant and animal breeding studies a distinction is made between the genetic value (additive + epistatic genetic effects) and the breeding value (additive genetic effects) of an individual since it is expected that some of the epistatic genetic effects will be lost due to recombination. In this paper, we argue that t…
New layer sparsity concept improves neural networks.
problem Improving neural network efficiency and interpretability.
method Formulated layer sparsity, introduced regularization and refitting schemes.
result Generated more compact and accurate neural networks.
Bayesian framework extracts features from high-dimensional spatio-temporal data.
problem Sparse structure and spatio-temporal dependence in high-dimensional data.
method Develops a Bayesian feature-extraction framework using Gaussian and Diffused-gamma priors, employing Bregman divergence likelihood and MCMC for posterior computation.
result Improves recovery of sparse features and enhances interpretability in the presence of spatio-temporal dependence.
Sparse Shape Reconstructionmath.FA
This paper introduces a new shape-based image reconstruction technique applicable to a large class of imaging problems formulated in a variational sense. Given a collection of shape priors (a shape dictionary), we define our problem as choosing the right elements and geometrically composing them through basic set opera…