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Found 9 papers, 1 papers with code
ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection
Class-incremental learning (CIL) learns a classification model with training data of different classes arising progressively.
Deterministic Bridge Regression for Compressive Classification
While the primal form is suitable for problems of low dimension with large data samples, the dual form is suitable for problems of high dimension but with a small number of data samples.
Analytic Learning of Convolutional Neural Network For Pattern Recognition
Training convolutional neural networks (CNNs) with back-propagation (BP) is time-consuming and resource-intensive particularly in view of the need to visit the dataset multiple times.
Accumulated Decoupled Learning: Mitigating Gradient Staleness in Inter-Layer Model Parallelization
Decoupled learning is a branch of model parallelism which parallelizes the training of a network by splitting it depth-wise into multiple modules.
Analytic Network Learning
Based on the property that solving the system of linear matrix equations via the column space and the row space projections boils down to an approximation in the least squares error sense, a formulation for learning the weight matrices of the multilayer network can be derived.
Gradient-Free Learning Based on the Kernel and the Range Space
In this article, we show that solving the system of linear equations by manipulating the kernel and the range space is equivalent to solving the problem of least squares error approximation.
Learning from the Kernel and the Range Space
In this article, a novel approach to learning a complex function which can be written as the system of linear equations is introduced.
Deterministic Stretchy Regression
An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper.
Stretchy Polynomial Regression
This article proposes a novel solution for stretchy polynomial regression learning.
