This survey includes systematic generalization and a history of how machine
learning addresses it. We aim to summarize and organize the related information
of both conventional and recent improvements. We first look at the definition
of systematic generalization, then introduce Classicist and Connectionist. We
then discuss different types of Connectionists and how they approach the
generalization. Two crucial problems of variable binding and causality are
discussed. We look into systematic generalization in language, vision, and VQA
fields. Recent improvements from different aspects are discussed. Systematic
generalization has a long history in artificial intelligence. We could cover
only a small portion of many contributions. We hope this paper provides a
background and is beneficial for discoveries in future work.

本研究系统地总结了机器学习中的系统化推广方法，并从经典派和联结派等不同角度介绍了其相关信息。我们重点讨论了不同类型的联结派及其如何处理推广问题，并探讨了在语言、视觉和 VQA 等领域中的应用。此外，我们还讨论了相关的变量绑定和因果问题，并简要介绍了最新的研究进展，希望能为未来的研究提供一定的背景和参考。

系统性归纳的简要调查

A Short Survey of Systematic Generalization

Is intelligence realized by connectionist or classicist? While connectionist
approaches have achieved superhuman performance, there has been growing
evidence that such task-specific superiority is particularly fragile in
systematic generalization. This observation lies in the central debate between
connectionist and classicist, wherein the latter continually advocates an
algebraic treatment in cognitive architectures. In this work, we follow the
classicist's call and propose a hybrid approach to improve systematic
generalization in reasoning. Specifically, we showcase a prototype with
algebraic representation for the abstract spatial-temporal reasoning task of
Raven's Progressive Matrices (RPM) and present the ALgebra-Aware
Neuro-Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract
algebra and the representation theory. It consists of a neural visual
perception frontend and an algebraic abstract reasoning backend: the frontend
summarizes the visual information from object-based representation, while the
backend transforms it into an algebraic structure and induces the hidden
operator on the fly. The induced operator is later executed to predict the
answer's representation, and the choice most similar to the prediction is
selected as the solution. Extensive experiments show that by incorporating an
algebraic treatment, the ALANS learner outperforms various pure connectionist
models in domains requiring systematic generalization. We further show the
generative nature of the learned algebraic representation; it can be decoded by
isomorphism to generate an answer.

本文探讨了连接主义和经典派对智能的实现方法，提出了一种混合方法来改进推理中的系统化泛化，通过代数表示来提高思维抽象能力，在 Raven's Progressive Matrices 任务中表现出比纯连接主义模型更好的推理性能，并且学到的代数表示具有生成能力。

学习代数表征以在抽象推理中进行系统化推广

Learning Algebraic Representation for Systematic Generalization in  Abstract Reasoning

Many high-performance models suffer from a lack of interpretability. There
has been an increasing influx of work on explainable artificial intelligence
(XAI) in order to disentangle what is meant and expected by XAI. Nevertheless,
there is no general consensus on how to produce and judge explanations. In this
paper, we discuss why techniques integrating connectionist and symbolic
paradigms are the most efficient solutions to produce explanations for
non-technical users and we propose a reasoning model, based on definitions by
Doran et al. [2017] (arXiv:1710.00794) to explain a neural network's decision.
We use this explanation in order to correct bias in the network's decision
rationale. We accompany this model with an example of its potential use, based
on the image captioning method in Burns et al. [2018] (arXiv:1803.09797).

本文讨论了为非技术用户产生解释的最有效和高效的解决方案，提出了一种基于连接主义和符号范式的推理模型来解释神经网络的决策，并用一个例子演示了它的潜在用途。