Deep learning has become a crucial technology for making breakthroughs in
many fields. Nevertheless, it still faces two important challenges in
theoretical and applied aspects. The first lies in the shortcomings of gradient
descent based learning schemes which are time-consuming and difficult to
determine the learning control hyperparameters. Next, the architectural design
of the model is usually tricky. In this paper, we propose a semi-adaptive
synergetic two-way pseudoinverse learning system, wherein each subsystem
encompasses forward learning, backward learning, and feature concatenation
modules. The whole system is trained using a non-gradient descent learning
algorithm. It simplifies the hyperparameter tuning while improving the training
efficiency. The architecture of the subsystems is designed using a data-driven
approach that enables automated determination of the depth of the subsystems.
We compare our method with the baselines of mainstream non-gradient descent
based methods and the results demonstrate the effectiveness of our proposed
method. The source code for this paper is available at
http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System}{this http URL

本文提出了一种半自适应协同双向伪逆学习系统，其每个子系统包含前向学习、反向学习和特征级联模块，并使用非梯度下降学习算法进行训练，简化了超参数调整，提高了训练效率。子系统的架构采用数据驱动方法设计，实现了子系统深度的自动确定。与基准的非梯度下降方法进行了比较，结果表明了本文方法的有效性。