Although neural models have achieved remarkable performance, they still
encounter doubts due to the intransparency. To this end, model prediction
explanation is attracting more and more attentions. However, current methods
rarely incorporate external knowledge and still suffer from three limitations:
(1) Neglecting concept completeness. Merely selecting concepts may not
sufficient for prediction. (2) Lacking concept fusion. Failure to merge
semantically-equivalent concepts. (3) Difficult in manipulating model behavior.
Lack of verification for explanation on original model. To address these
issues, we propose a novel knowledge-aware neuron interpretation framework to
explain model predictions for image scene classification. Specifically, for
concept completeness, we present core concepts of a scene based on knowledge
graph, ConceptNet, to gauge the completeness of concepts. Our method,
incorporating complete concepts, effectively provides better prediction
explanations compared to baselines. Furthermore, for concept fusion, we
introduce a knowledge graph-based method known as Concept Filtering, which
produces over 23% point gain on neuron behaviors for neuron interpretation. At
last, we propose Model Manipulation, which aims to study whether the core
concepts based on ConceptNet could be employed to manipulate model behavior.
The results show that core concepts can effectively improve the performance of
original model by over 26%.

通过引入外部知识和核心概念，我们提出了一种新颖的知识感知神经元解释框架，用于解释图像场景分类模型的预测结果。我们的方法相比基线模型在解释性能上提供了更好的预测解释，并且核心概念能够有效地改善原始模型的性能。

基于知识的场景分类神经元解释

Knowledge-Aware Neuron Interpretation for Scene Classification

This document facilitates understanding of core concepts about uniform
B-spline and its matrix representation.

统一 B 样条、核心概念、矩阵表示。

统一的 B 样条教程

A Tutorial on Uniform B-Spline

Disentangling the factors of variation in data is a fundamental concept in
machine learning and has been studied in various ways by different researchers,
leading to a multitude of definitions. Despite the numerous empirical studies,
more theoretical research is needed to fully understand the defining properties
of disentanglement and how different definitions relate to each other. This
paper presents a meta-analysis of existing definitions of disentanglement,
using category theory as a unifying and rigorous framework. We propose that the
concepts of the cartesian and monoidal products should serve as the core of
disentanglement. With these core concepts, we show the similarities and crucial
differences in dealing with (i) functions, (ii) equivariant maps, (iii)
relations, and (iv) stochastic maps. Overall, our meta-analysis deepens our
understanding of disentanglement and its various formulations and can help
researchers navigate different definitions and choose the most appropriate one
for their specific context.

本文通过范畴论作为一个统一严密框架，元分析了现有的解缠结概念定义，并认为笛卡尔积和幺模乘积应作为解缠结的核心概念，从函数、等变映射、关系和随机映射的角度展示了不同处理方式的相似性和重要差异。