SOTAVerified

Sparse Learning

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 185 papers

TitleStatusHype
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
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
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
Best Subset Selection with Efficient Primal-Dual Algorithm—0
A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model—0
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations—0
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications—0
Infinite-Dimensional Sparse Learning in Linear System Identification—0
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention—0
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning—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