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.
翻译:人类拥有将序列分解成其组成部分的固有能力。 事实上, 人们认为这种能力可以吸引语言技能, 并学习图像模式, 这可能是更像动物的智能类型的关键。 在这里, 我们建议持续地对块状问题( 不受监督的问题) 进行概括化, 包括固定的和概率性的块块, 发现时间和因果关系结构及其持续的变化。 此外, 我们提出一个名为 SyncMap 的算法, 它可以通过创建动态地图来学习和适应问题的变化, 保存变量之间的关联。 SyncMap 的结果表明, 拟议的算法可以学习近乎最佳的解决方案, 尽管存在许多类型的结构及其持续的变化。 与Word2vec、 PARSER和MRIL相比, Syncap 超过或连接关于假设情景中66美元的最佳算法, 而在剩下的34美元中是第二最佳算法。 SyncMzifel/ Synap 的无损失函数表明, 也许令人惊讶的是, 仅靠自我组织就能完成很多事情。 在 https://github.com/zwif/ Synap/ Synap 中可以查阅的代码。