论文标题
持续的一般块问题和同步
Continual General Chunking Problem and SyncMap
论文作者
论文摘要
人类具有固有的能力，可以将序列分为组成部分。实际上，这种能力被认为可以引起语言技能和图像模式的学习，这可能是一种更像动物般的智力类型的关键。在这里，我们提出了对分解问题的持续概括（一个无监督的问题），包括固定和概率的块，发现时间和因果结构及其持续变化。此外，我们提出了一种称为syncmap的算法，可以通过创建一个保持变量之间相关性的动态映射来学习和适应问题的变化。 Syncmap的结果表明，尽管存在多种类型的结构及其持续变化，但提出的算法在接近最佳溶液附近学习。与Word2Vec，解析器和MRIL相比，SyncMap在$ 66 \％$的情况下超过了最佳算法，同时在剩下的34美元\％$中是第二好。 Syncmap的无模型简单动力学和缺乏损失函数表明，单独进行自组织可以做很多事情。可在https://github.com/zweifel/syncmap上找到代码。
Humans possess an inherent ability to chunk sequences into their constituent parts. In fact, this ability is thought to bootstrap language skills and learning of image patterns which might be a key to a more animal-like type of intelligence. Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations. Additionally, we propose an algorithm called SyncMap that can learn and adapt to changes in the problem by creating a dynamic map which preserves the correlation between variables. Results of SyncMap suggest that the proposed algorithm learn near optimal solutions, despite the presence of many types of structures and their continual variation. When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on $66\%$ of the scenarios while being the second best in the remaining $34\%$. SyncMap's model-free simple dynamics and the absence of loss functions reveal that, perhaps surprisingly, much can be done with self-organization alone. Code available at https://github.com/zweifel/SyncMap.
