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Sparse Learning

Papers

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Showing 76–100 of 185 papers

TitleStatusHype
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 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
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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