New method classifies p>>n data using sparse nPCA.
problem Classifying p>>n data in medical imaging and genetics.
method Linear discriminant analysis with sparse nPCA for variable selection.
result The new method outperforms traditional methods in experiments.
Sparse MDP with entropy regularization improves reinforcement learning performance.
problem Improving reinforcement learning policies with sparse and multi-modal distributions.
method Proposes a sparse Markov decision process with causal sparse Tsallis entropy regularization.
result The proposed method achieves a constant performance error bound, outperforming soft MDPs.
Sparse Hopfield model improves memory retrieval with fewer connections.
problem Memory retrieval efficiency with fewer connections.
method Sparse extension of Hopfield model, derived from sparse entropic regularizer.
result Sparse Hopfield model achieves tighter error bounds and better performance.
New quantum code lacks sparse lift.
problem Existence of sparse lifts for quantum codes.
method Constructed a sparse Z2 chain complex without a sparse lift. result Found a quantum code without a sparse lift.
Hierarchical Block Sparse Neural Networks improve both accuracy and runtime efficiency of sparse DNNs.
problem Inefficiency of sparse DNNs on regular parallel hardware due to irregular computation.
method Introducing HBsNN, a structured sparse neural network that balances accuracy and runtime efficiency.
result HBsNN achieves better runtime performance and accuracy than unstructured and highly structured sparse models.
This work introduces a method to compare sparse neural network topologies using graph theory.
problem Comparing and understanding sparse neural network topologies, especially during training.
method Introducing Neural Network Sparse Topology Distance (NNSTD) to measure distances between different sparse neural networks.
result Sparse neural networks can outperform over-parameterized models without further structure optimization.
Sparse-RS framework efficiently attacks models with sparse perturbations.
problem Efficiently attacking models with sparse perturbations in the black-box setting.
method Random search-based Sparse-RS framework for score-based attacks.
result Sparse-RS achieves state-of-the-art success rate and query efficiency.
Using a Bayesian approach, we consider the problem of recovering sparse signals under additive sparse and dense noise. Typically, sparse noise models outliers, impulse bursts or data loss. To handle sparse noise, existing methods simultaneously estimate the sparse signal of interest and the sparse noise of no interest.…
Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.
problem Scalability issues in Sparse Deep Neural Networks (DNNs).
method Mathematically defined DNN inference computation, community submissions from various fields.
result Sparse DNN execution time, TmDNN, is strongly dependent on the number of operations, Nmop. In compressed sensing, we wish to reconstruct a sparse signal x from observed data y. In sparse coding, on the other hand, we wish to find a representation of an observed signal y as a sparse linear combination, with coefficients x, of elements from an overcomplete dictionary. While many algorithms are competit…
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…
Sparse learning speeds up neural network training without sacrificing accuracy.
problem Training deep neural networks efficiently while maintaining performance.
method Sparse momentum algorithm that redistributes and grows weights based on momentum magnitude.
result State-of-the-art sparse performance on various datasets with up to 5.61x faster training.
Paper presents a new method for solving sparse learning problems.
problem Sparse learning challenges in high-dimensional data analysis.
method Parametric Simplex Method (PSM) for solving linear programs parametrized by a regularization factor.
result PSM offers advantages over competing methods in terms of solution path, precision, and computational efficiency.
Dynamic Sparse Training finds efficient sparse networks from scratch.
problem Finding efficient sparse neural networks.
method Jointly optimizes network parameters and sparsity with trainable thresholds.
result Achieves state-of-the-art performance with minimal performance loss.
Sparse coding improves reinforcement learning representations.
problem Improving representation learning in reinforcement learning.
method Developed a supervised sparse coding objective for policy evaluation.
result Sparse coding representations outperform tile-coding representations.
Sparse group Lasso optimizes sparse and grouped parameters in high-dimensional data.
problem Simultaneously sparse and grouped parameters in high-dimensional linear regression.
method Sparse group Lasso, debiased sparse group Lasso, statistical inference.
result Matching upper and lower bounds on sample complexity and estimation error.
Graph-Sparse Logistic Regression for sparse and connected support classification.
problem Sparse and connected support classification problems.
method Introduces Graph-Sparse Logistic Regression algorithm.
result Validated and benchmarked against L1-regularized Logistic Regression.
New method for hyperparameter tuning in sparse matrix factorization.
problem Hyperparameter tuning in sparse matrix factorization.
method Numerical method based on evaluating the zero point of normalization factor in sparse matrix prior.
result Our method outperforms existing algorithms in ground-truth sparse matrix reconstruction.
This paper explores loss landscapes of sparse neural networks, finding unique characteristics compared to dense networks.
problem Understanding the loss landscape of sparse neural networks, especially one-hidden-layer networks.
method Analyzes sparse networks with dense and sparse final layers, focusing on linear and non-linear models.
result Sparse networks can have no spurious valleys under certain conditions, but spurious valleys and minima can exist for wide sparse networks.
Sparse APCA identifies sparse factors in financial returns over time.
problem Analyzing co-movements of high-dimensional panel data over time.
method Sparse asymptotic PCA with truncated power method for sparse factors and sequential deflation for multi-factor cases.
result Identification of nine risk factors influencing the S&P 500 stock market.
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.
A new method for sparse PCA using orthogonal rotations and soft-thresholding.
problem Sparse PCA with a new basis using orthogonal rotations.
method Initialize with leading principal components, apply kimesk orthogonal rotation, and soft-threshold the rotated components. result The proposed method is more stable and explains more variance compared to alternatives.
DS-Softmax speeds up softmax inference by learning sparse experts.
problem Expensive softmax computations for large output classes.
method Sparse mixture of sparse experts for efficient top-k class retrieval.
result Significant computation reductions achieved at no performance loss.
Develops sparse portfolio strategy for high-dimensional assets.
problem Sparse wealth allocations in high dimensions are limited by existing approaches.
method Establishes theoretical bounds and empirical analysis of sparse weight estimators.
result Sparse portfolios are robust to recessions and can be used as a hedging vehicle.
TSN improves sparse signal recovery with less complexity.
problem Sparse regression problem of recovering sparse signals from measurements.
method Tree search algorithm driven by deep neural network with pruning.
result TSN outperforms conventional methods in various sensing matrices.
Picasso is a new library for sparse learning problems in R and Python.
problem Sparse learning problems in high-dimensional data analysis.
method Unified framework of pathwise coordinate optimization with efficient active set selection strategies.
result picasso can efficiently handle large-scale problems.
The CUR decomposition provides an approximation of a matrix X that has low reconstruction error and that is sparse in the sense that the resulting approximation lies in the span of only a few columns of X. In this regard, it appears to be similar to many sparse PCA methods. However, CUR takes a randomized algorithm…
SpInGP speeds up Gaussian process computations with sparse matrices.
problem Efficiently computing Gaussian processes for large datasets.
method Sparse precision Gaussian process formulation and parallelizable matrix routines.
result The parallelized SpInGP reduces time complexity to sublinear.
DFR reduces the computational cost of sparse-group lasso and adaptive sparse-group lasso.
problem Sparse-group lasso's computational expense and need for tuning.
method Dual Feature Reduction (DFR) using strong screening rules and dual norms.
result DFR drastically reduces computational cost without affecting solution optimality.
Method converts sparse systems to dense ones for statistical mechanics problems.
problem Statistical mechanics on sparse graphs
method Extracts a Feedback Vertex Set, learns variational distribution, estimates free energy.
result More accurate and faster than existing methods for sparse systems.
Simpler approach for sparse clustering.
problem Sparse clustering with only useful features.
method Hill-climbing approach to Sparse K-means.
result Competitive with COSA and Sparse K-means.
New robust PCA algorithm for matrices with both sparse and outlying elements.
problem Simultaneous sparse and outlying corruption in matrices.
method Sparse approximation of a sparsely corrupted column to distinguish inliers from outliers.
result Robust PCA algorithm can handle both sparse and outlying corruptions.
Efficiently estimates sparse functionals robustly in high dimensions.
problem Statistical procedures are sensitive to minor deviations in high-dimensional settings.
method Proposes a computationally and statistically efficient algorithm for robust estimation of sparse functionals.
result Guarantees accurate recovery of sparse functionals under certain deterministic conditions.
Sparse JL with higher sparsity improves feature hashing accuracy.
problem Efficiently reducing high-dimensional feature vectors to lower dimensions.
method Sparse Johnson-Lindenstrauss transform with varying sparsity levels.
result Sparse JL with sparsity greater than 1 provides better norm preservation.
Guarantees sparse recovery for neural networks with iterative hard thresholding.
problem Recovering sparse network weights in neural networks.
method Structural properties of sparse network weights and iterative hard thresholding algorithm.
result Simple iterative hard thresholding algorithm recovers sparse network weights exactly using linear memory.
New methods show sparse portfolios offer no advantage over mean-variance in diversification.
problem Investment diversification and risk management with sparse portfolios.
method Developed and implemented a new estimation procedure for sparse second-order stochastic spanning using a greedy algorithm and Linear Programming.
result No benefit from expanding a sparse opportunity set beyond 45 assets; optimal sparse portfolio reduces tail risk.
Sparse representations improve reinforcement learning control policies.
problem Sparse representations are underused in reinforcement learning control.
method Incremental learning with sparse representations from neural networks, using distributional regularizers.
result Sparse representations avoid catastrophic interference and provide stable values for reinforcement learning.
Sparse-TDA combines TDA and sparse sampling for multi-way classification.
problem Reconstructing shapes from high-dimensional data for multi-way classification.
method Sparse-TDA algorithm that selects sparse pixel samples from persistent features using QR pivoting.
result Sparse-TDA demonstrates promising performance on human posture recognition and image texture classification.
Survey on greedy algorithms for sparse approximation.
problem Sparse approximation using greedy algorithms.
method Greedy algorithms, Lebesgue-type inequalities, trigonometric system, tensor product structure.
result Constructive sparse approximation methods provided for various cases.
Sparse manifold transform linearizes non-linear signal transformations.
problem Non-linear signal transformations in sensory data.
method Combines sparse coding, manifold learning, and slow feature analysis.
result Models sparse discreteness and low-dimensional manifold structure in natural scenes.
msPCA solves sparse PCA for multiple components efficiently.
problem Sparse principal component analysis with multiple components.
method Alternating maximization algorithm for sparse loading vectors, with orthogonality or zero correlation constraints.
result Achieves high variance explained with sparse components and controlled feasibility violations.
New method recovers block-sparse signals with common sparsity patterns.
problem Recovering block-sparse signals with common sparsity patterns in MMV.
method Pattern-coupled hierarchical Gaussian prior model with EM framework.
result Proposed method automatically captures block sparse structure.
We consider the scenario where one observes an outcome variable and sets of features from multiple assays, all measured on the same set of samples. One approach that has been proposed for dealing with this type of data is ``sparse multiple canonical correlation analysis'' (sparse mCCA). All of the current sparse mCCA t…
Predictive Sparse Manifold Transform learns dynamic video sequences.
problem Learning and predicting natural dynamics in video sequences.
method Two-layer framework: sparse coding and manifold learning.
result PSMT with dynamic embedding space outperforms static baselines in future frame prediction.
New method finds sparse networks without labels, improving performance.
problem Sparse connectivity in neural networks to reduce memory and energy demands.
method Neural Tangent Transfer method to find sparse networks without labels.
result Sparse networks achieve higher classification performance and faster convergence.
Sparse filtering adapted for covariate shift with periodic structure.
problem Adapting to covariate shift with sparse filtering.
method Theoretical analysis and development of periodic sparse filtering.
result Sparse filtering can adapt under periodic structure, not just cosine metric.
Efficiently prices options in stochastic volatility models using sparse grids.
problem Option pricing in stochastic volatility models.
method Sparse grid high-order ADI scheme for second-order in time and fourth-order in space.
result Computational efficiency gains confirmed by numerical experiments.
Paper introduces efficient algorithm for double-sparse coding with theoretical guarantees.
problem High storage and processing costs in sparse coding for high-dimensional data.
method Simple algorithm for double-sparse coding, leveraging neural architectures for efficiency.
result Theoretical analysis shows asymptotic sample complexity and running time benefits over existing methods.