SOTAVerified

Sparse Learning

Papers

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Showing 26–50 of 185 papers

TitleStatusHype
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
Dynamic Incremental Optimization for Best Subset Selection—0
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines—0
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees—0
Building explainable graph neural network by sparse learning for the drug-protein binding predictionCode0
HyperSparse Neural Networks: Shifting Exploration to Exploitation through Adaptive RegularizationCode0
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural NetworksCode0
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization—0
Discovering stochastic partial differential equations from limited data using variational Bayes inference—0
Classical Verification of Quantum Learning—0
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective—0
Learning to Super-Resolve Blurry Images with EventsCode1
Video-Text Retrieval by Supervised Sparse Multi-Grained LearningCode0
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks—0
SuperGF: Unifying Local and Global Features for Visual Localization—0
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity—0
Learning sparse auto-encoders for green AI image coding—0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Learning governing physics from output only measurements—0
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love ConstraintsCode1
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems—0
Attribute Graphs Underlying Molecular Generative Models: Path to Learning with Limited Data—0
Sparsifying Binary Networks—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