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Sparse Learning

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 185 papers

TitleStatusHype
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex PenaltiesCode0
Learning task structure via sparsity grouped multitask learningCode0
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive RegularizationCode0
Can Image-Level Labels Replace Pixel-Level Labels for Image Parsing—0
Fast Learning with Nonconvex L1-2 Regularization—0
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems—0
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention—0
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention—0
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning—0
Beyond L2-Loss Functions for Learning Sparse Models—0
A Knowledge Transfer Framework for Differentially Private Sparse Learning—0
Efficient Mixed-Norm Regularization: Algorithms and Safe Screening Methods—0
Efficient Distributed Learning with Sparsity—0
Best Subset Selection with Efficient Primal-Dual Algorithm—0
Effective Proximal Methods for Non-convex Non-smooth Regularized Learning—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
Best Subset Selection via a Modern Optimization Lens—0
Dynamic Incremental Optimization for Best Subset Selection—0
Discovering stochastic partial differential equations from limited data using variational Bayes inference—0
Direct l_(2,p)-Norm Learning for Feature Selection—0
Bayesian Sparse learning with preconditioned stochastic gradient MCMC and its applications—0
Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph—0
Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models—0
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries—0
Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems—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