Sparse Learning
44 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
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.
Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective
The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation.
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs.
SuperGF: Unifying Local and Global Features for Visual Localization
In this study, we present a novel method called SuperGF, which effectively unifies local and global features for visual localization, leading to a higher trade-off between localization accuracy and computational efficiency.
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling.
Learning sparse auto-encoders for green AI image coding
Recently, convolutional auto-encoders (CAE) were introduced for image coding.
Learning governing physics from output only measurements
The existing techniques for equations discovery are dependent on both input and state measurements; however, in practice, we only have access to the output measurements only.
AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems
In this work, we propose an adaptive sparse learning algorithm that can be applied to learn the physical processes and obtain a sparse representation of the solution given a large snapshot space.
Sparsifying Binary Networks
Our experiments confirm that SBNNs can achieve high compression rates, without compromising generalization, while further reducing the operations of BNNs, making SBNNs a viable option for deploying DNNs in cheap, low-cost, limited-resources IoT devices and sensors.
Best Subset Selection with Efficient Primal-Dual Algorithm
Best subset selection is considered the `gold standard' for many sparse learning problems.
