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}{http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System.

翻译：深度学习已成为推动众多领域取得突破的关键技术。然而，其在理论与应用层面仍面临两个重要挑战。其一在于基于梯度下降的学习方案存在耗时且难以确定学习控制超参数的缺陷。其次，模型的结构设计通常较为棘手。本文提出一种半自适应协同双向伪逆学习系统，其中每个子系统均包含前向学习、后向学习及特征拼接模块。整个系统采用非梯度下降学习算法进行训练，在提升训练效率的同时简化了超参数调优过程。子系统的架构通过数据驱动方法设计，能够自动确定子系统的深度。我们将所提方法与主流非梯度下降基线的基准方法进行比较，结果验证了本方法的有效性。本文源代码发布于 http://github.com/B-berrypie/Semi-adaptive-Synergetic-Two-way-Pseudoinverse-Learning-System。