论文标题
预测错误驱动的内存整合，用于持续学习。关于自适应温室模型
Prediction error-driven memory consolidation for continual learning. On the case of adaptive greenhouse models
论文作者
论文摘要
这项工作提出了一种自适应体系结构，该体系结构可以进行在线学习，并通过情节记忆和预测驱动的记忆合并面临灾难性的遗忘问题。根据认知科学和神经科学的证据，保留了记忆，这取决于它们与系统中存储的先验知识的一致性。这是根据生成模型产生的预测误差来估计的。此外，该AI系统被转移到园艺行业的创新应用：温室模型的学习和转移。这项工作提出了一种模型，该模型训练了从研究设施记录的数据并转移到生产温室的模型。
This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation. In line with evidences from the cognitive science and neuroscience, memories are retained depending on their congruency with the prior knowledge stored in the system. This is estimated in terms of prediction error resulting from a generative model. Moreover, this AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents a model trained on data recorded from research facilities and transferred to a production greenhouse.
