Sparse Learning
43 papers with code • 3 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Sparse Learning
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Libraries
Use these libraries to find Sparse Learning models and implementations
ImageNet
CINIC-10
ImageNet-32
Latest papers with no code
CRB Analysis for Mixed-ADC Based DOA Estimation
The Cram{\'e}r-Rao bound (CRB) with the antenna-varying threshold is obtained.
Learning with Diversification from Block Sparse Signal
This paper introduces a novel prior called Diversified Block Sparse Prior to characterize the widespread block sparsity phenomenon in real-world data.
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment.
Dynamic Incremental Optimization for Best Subset Selection
Best subset selection is considered the `gold standard' for many sparse learning problems.
Sparse Learning and Class Probability Estimation with Weighted Support Vector Machines
The binary class probability is then estimated either by the $\ell^2$-norm regularized wSVMs framework with selected variables or by elastic net regularized wSVMs directly.
Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees
We present general theoretical result for the approximation error between the optimization results of the original problem and the sketched problem for regularized least square problems which can be convex or nonconvex.
Discovering stochastic partial differential equations from limited data using variational Bayes inference
We propose a novel framework for discovering Stochastic Partial Differential Equations (SPDEs) from data.
Classical Verification of Quantum Learning
Finally, we showcase two general scenarios in learning and verification in which quantum mixture-of-superpositions examples do not lead to sample complexity improvements over classical data.
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
Sparsity of a learning solution is a desirable feature in machine learning.
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources.
