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Sparse Learning

Papers

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Showing 51–100 of 185 papers

TitleStatusHype
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation—0
GUIDE: Real-Time Human-Shaped Agents—0
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data—0
Homotopy Parametric Simplex Method for Sparse Learning—0
HONOR: Hybrid Optimization for NOn-convex Regularized problems—0
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Approximate Post-Selective Inference for Regression with the Group LASSO—0
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations—0
Infinite-Dimensional Sparse Learning in Linear System Identification—0
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks—0
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity—0
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model—0
Accelerated Mini-batch Randomized Block Coordinate Descent Method—0
Accelerated Training for Matrix-norm Regularization: A Boosting Approach—0
Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models—0
Adaptive Neural Connections for Sparsity Learning—0
Adaptive support driven Bayesian reweighted algorithm for sparse signal recovery—0
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks—0
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning—0
A Knowledge Transfer Framework for Differentially Private Sparse Learning—0
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems—0
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning—0
A New Data-Driven Sparse-Learning Approach to Study Chemical Reaction Networks—0
A Bayesian Lasso based Sparse Learning Model—0
A Nonconvex Approach for Structured Sparse Learning—0
Approximate Message Passing with Consistent Parameter Estimation and Applications to Sparse Learning—0
A Sparse Learning Approach to the Design of Radar Tunable Architectures with Enhanced Selectivity Properties—0
A unified approach to mixed-integer optimization problems with logical constraints—0
Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph—0
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications—0
Best Subset Selection via a Modern Optimization Lens—0
Best Subset Selection with Efficient Primal-Dual Algorithm—0
Safe Feature Pruning for Sparse High-Order Interaction Models—0
Safe Screening With Variational Inequalities and Its Application to LASSO—0
Scaling Continuous Kernels with Sparse Fourier Domain Learning—0
A Discriminative Gaussian Mixture Model with Sparsity—0
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models—0
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees—0
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization—0
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines—0
Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach—0
Sparse Learning for Variable Selection with Structures and Nonlinearities—0
Structure learning via unstructured kernel-based M-regression—0
Sparse learning of maximum likelihood model for optimization of complex loss function—0
Sparse Learning over Infinite Subgraph Features—0
Sparse learning with CART—0
Sparse Range-constrained Learning and Its Application for Medical Image Grading—0
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces—0
Sparsified-Learning for Heavy-Tailed Locally Stationary Processes—0
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All datasetsImageNetCINIC-10ImageNet32

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Resnet-50: 80% SparseTop-1 Accuracy77.1—Unverified
2Resnet-50: 90% SparseTop-1 Accuracy76.4—Unverified
3Resnet-50: 80% Sparse 100 epochsTop-1 Accuracy76—Unverified
4Resnet-50: 80% Sparse 100 epochsTop-1 Accuracy75.84—Unverified
5Resnet-50: 90% Sparse 100 epochsTop-1 Accuracy74.5—Unverified
6Resnet-50: 90% Sparse 100 epochsTop-1 Accuracy73.82—Unverified
7MobileNet-v1: 75% SparseTop-1 Accuracy71.9—Unverified
8MobileNet-v1: 90% SparseTop-1 Accuracy68.1—Unverified
9SINDyTop-1 Accuracy6—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Sparsity92.43—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet18Sparsity93.63—Unverified