SOTAVerified

Sparse Learning

Papers

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Showing 51–75 of 185 papers

TitleStatusHype
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
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model TuningCode1
SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications—0
APP: Anytime Progressive PruningCode1
Infinite-Dimensional Sparse Learning in Linear System Identification—0
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention—0
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning—0
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention—0
SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse LearningCode0
Learning where to learn: Gradient sparsity in meta and continual learningCode1
abess: A Fast Best Subset Selection Library in Python and RCode1
Multi-Slice Dense-Sparse Learning for Efficient Liver and Tumor Segmentation—0
High Dimensional Differentially Private Stochastic Optimization with Heavy-tailed Data—0
Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression—0
Improving Sequential Recommendation Consistency with Self-Supervised Imitation—0
Sparse Training via Boosting Pruning Plasticity with NeuroregenerationCode1
Surrogate assisted active subspace and active subspace assisted surrogate -- A new paradigm for high dimensional structural reliability analysis—0
Grouped Variable Selection with Discrete Optimization: Computational and Statistical PerspectivesCode0
Learning where to learn—0
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse TrainingCode1
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Federated Nonconvex Sparse 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