SOTAVerified

Sparse Learning

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 185 papers

TitleStatusHype
SWAG: A Wrapper Method for Sparse Learning—0
Tensor Representation and Manifold Learning Methods for Remote Sensing Images—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression—0
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach—0
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity—0
Optimal Stochastic and Online Learning with Individual Iterates—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
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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