SOTAVerified

Sparse Learning

Papers

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Showing 1–25 of 185 papers

TitleStatusHype
Scalable Subset Selection in Linear Mixed ModelsCode0
Learning Sparsity for Effective and Efficient Music Performance Question Answering—0
Optimizing Hard Thresholding for Sparse Model Discovery—0
STARS: Sparse Learning Correlation Filter with Spatio-temporal Regularization and Super-resolution Reconstruction for Thermal Infrared Target Tracking—0
Sparsified-Learning for Heavy-Tailed Locally Stationary Processes—0
Low-Rank Matrix Regression via Least-Angle Regression—0
Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
MyESL: Sparse learning in molecular evolution and phylogenetic analysisCode0
Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning—0
GUIDE: Real-Time Human-Shaped Agents—0
Scaling Continuous Kernels with Sparse Fourier Domain Learning—0
Optimizing Rare Word Accuracy in Direct Speech Translation with a Retrieval-and-Demonstration ApproachCode0
Quasi-potential and drift decomposition in stochastic systems by sparse identification—0
Probabilistic Iterative Hard Thresholding for Sparse LearningCode0
Multi-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networks—0
ssProp: Energy-Efficient Training for Convolutional Neural Networks with Scheduled Sparse Back PropagationCode0
DRFormer: Multi-Scale Transformer Utilizing Diverse Receptive Fields for Long Time-Series ForecastingCode0
Generative Adversarial Networks for Imputing Sparse Learning Performance—0
SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretrainingCode1
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Nonparametric Sparse Online Learning of the Koopman Operator—0
CLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity PromotionCode0
CRB Analysis for Mixed-ADC Based DOA Estimation—0
Block Sparse Bayesian Learning: A Diversified SchemeCode1
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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