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Found 4 papers, 2 papers with code
Only Train Once: A One-Shot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural networks (DNNs) onto resource-constrained devices.
Neural Network Compression Via Sparse Optimization
The compression of deep neural networks (DNNs) to reduce inference cost becomes increasingly important to meet realistic deployment requirements of various applications.
Orthant Based Proximal Stochastic Gradient Method for $\ell_1$-Regularized Optimization
Sparsity-inducing regularization problems are ubiquitous in machine learning applications, ranging from feature selection to model compression.
Detecting Relative Anomaly
System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems.
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