Neural-symbolic approaches to machine learning incorporate the advantages
from both connectionist and symbolic methods. Typically, these models employ a
first module based on a neural architecture to extract features from complex
data. Then, these features are processed as symbols by a symbolic engine that
provides reasoning, concept structures, composability, better generalization
and out-of-distribution learning among other possibilities. However, neural
approaches to the grounding of symbols in sensory data, albeit powerful, still
require heavy training and tedious labeling for the most part. This paper
presents a new symbolic-only method for the generation of hierarchical concept
structures from complex spatial sensory data. The approach is based on
Bateson's notion of difference as the key to the genesis of an idea or a
concept. Following his suggestion, the model extracts atomic features from raw
data by computing elemental sequential comparisons in a stream of multivariate
numerical values. Higher-level constructs are built from these features by
subjecting them to further comparisons in a recursive process. At any stage in
the recursion, a concept structure may be obtained from these constructs and
features by means of Formal Concept Analysis. Results show that the model is
able to produce fairly rich yet human-readable conceptual representations
without training. Additionally, the concept structures obtained through the
model (i) present high composability, which potentially enables the generation
of 'unseen' concepts, (ii) allow formal reasoning, and (iii) have inherent
abilities for generalization and out-of-distribution learning. Consequently,
this method may offer an interesting angle to current neural-symbolic research.
Future work is required to develop a training methodology so that the model can
be tested against a larger dataset.

该论文提出了一种新的基于晦涩数据生成分层概念结构的符号方法，不需要训练即能生成丰富易读的概念表示，并具有高组合性、正式推理和泛化和分布式学习的内在能力。

递归贝茨逊模型生成基于空间感觉数据的语义形式概念

A Recursive Bateson-Inspired Model for the Generation of Semantic Formal  Concepts from Spatial Sensory Data

Out-of-distribution (OOD) learning deals with scenarios in which training and
test data follow different distributions. Although general OOD problems have
been intensively studied in machine learning, graph OOD is only an emerging
area of research. Currently, there lacks a systematic benchmark tailored to
graph OOD method evaluation. In this work, we aim at developing an OOD
benchmark, known as GOOD, for graphs specifically. We explicitly make
distinctions between covariate and concept shifts and design data splits that
accurately reflect different shifts. We consider both graph and node prediction
tasks as there are key differences in designing shifts. Overall, GOOD contains
11 datasets with 17 domain selections. When combined with covariate, concept,
and no shifts, we obtain 51 different splits. We provide performance results on
10 commonly used baseline methods with 10 random runs. This results in 510
dataset-model combinations in total. Our results show significant performance
gaps between in-distribution and OOD settings. Our results also shed light on
different performance trends between covariate and concept shifts by different
methods. Our GOOD benchmark is a growing project and expects to expand in both
quantity and variety of resources as the area develops. The GOOD benchmark can
be accessed via this https URL.

本研究旨在开发用于图形的 OOD 基准（称为 GOOD），以区分协变和概念变化并设计准确反映不同变化的数据拆分。在 10 个常用的基线方法上提供性能结果，结果表明在分布内和 OOD 设置之间存在显着性能差距，本基准将不断发展并扩大资源的数量和种类。