An automated construction of a denoising autoeconder is presented here. It features an open structure both in the generative phase and in the discriminative phase where input features can be automatically added and discarded on the fly and free of the problem- specific threshold.

This code refers to all experiments in our paper "Autonomous Deep Learning: Continual Learning Approach for Dynamic Environments"

Modified version of ADL and DEVDAN: Implement multiple node growing scenario and adaptive synapses pruning

本教師データの導入により、ワーキングメモリはAIにおいて「心」の生成・維持を担う基盤的機構として機能すると考えられます。The introduction of this training data allows the working memory to function as a foundational mechanism responsible for the generation and maintenance of what may be considered a “mind” within AI systems.

本教師データの導入により、ワーキングメモリはAIにおいて「心」の生成・維持を担う基盤的機構として機能すると考えられます。The introduction of this training data allows the working memory to function as a foundational mechanism responsible for the generation and maintenance of what may be considered a “mind” within AI systems.