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Found 4 papers, 0 papers with code
Siamese Labels Auxiliary Learning
In general, the main work of this paper include: (1) propose SiLa Learning, which improves the performance of common models without increasing test parameters; (2) compares SiLa with DML and proves that SiLa can improve the generalization of the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa can be used for various types of network structures.
Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs
Specifically, when q > k, the proposed algorithm computes only a k * k matrix inverse, instead of a q * q matrix inverse in the existing algorithm.
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded.
Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces
A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out to be a typical extremal problem located on the upper upper-high complex plane.
