建立一个通用的连续知识库
Towards a Universal Continuous Knowledge Base
摘要：
在人工智能（AI）中，知识是智能系统完成任务所需的信息。虽然传统的知识库使用离散的，符号表示，检测知识编码的连续表示从数据中学习最近受到越来越多的关注。在这项工作中，我们提出了一种方法，用于建立一个连续的知识库（CKB），可以存储从多个，不同的神经网络导入的知识。该方法的核心思想是为每个神经网络定义一个接口，并将知识传递问题转化为函数模拟问题。文本分类实验表明，CKB从一个单一的模型中导入知识，然后将知识导出到一个新的模型，实现了与原始模型相当的性能。更有趣的是，我们将多个模型中的知识导入到知识库中，融合后的知识从知识库中导出回单个模型，从而实现了比原始模型更高的准确性。有了CKB，也很容易实现知识蒸馏和迁移学习。我们的工作为构建一个通用的连续知识库打开了大门，以收集、存储和组织编码在各种神经网络中的所有连续知识，这些神经网络是为不同的人工智能任务训练的。
英文摘要：
In artificial intelligence (AI), knowledge is the information required by an intelligent system to accomplish tasks. While traditional knowledge bases use discrete, symbolic representations, detecting knowledge encoded in the continuous representations learned from data has received increasing attention recently. In this work, we propose a method for building a continuous knowledge base (CKB) that can store knowledge imported from multiple, diverse neural networks. The key idea of our approach is to define an interface for each neural network and cast knowledge transferring as a function simulation problem. Experiments on text classification show promising results: the CKB imports knowledge from a single model and then exports the knowledge to a new model, achieving comparable performance with the original model. More interesting, we import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to a single model, achieving a higher accuracy than the original model. With the CKB, it is also easy to achieve knowledge distillation and transfer learning. Our work opens the door to building a universal continuous knowledge base to collect, store, and organize all continuous knowledge encoded in various neural networks trained for different AI tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
