论文标题
浸入嵌入字段中的有向多画的浸入。概括
The Immersion of Directed Multi-graphs in Embedding Fields. Generalisations
论文作者
论文摘要
本文的目的是概述一个广义模型，用于表示关系类别，符号，感知 - 感官和感知贴边数据的混合，以便在相同的体系结构数据层中体现输入，输出和潜在张紧器的表示。目前，各种机器学习模型在计算机视觉，NLP/NLU，增强学习中使用了这种多种表示形式，该模型允许直接应用跨域查询和功能。这是通过赋予至少某些边缘属性的有向张量的多画图来实现的，这些属性代表来自各个潜在空间的嵌入，以定义，构建和计算新的相似性和之间的新相似性和距离形式之间的新相似性和距离关系，包括视觉，语言，听觉的潜在表示，从而插入了统一的统计视图，从而插入了统一的统计视图。
The purpose of this paper is to outline a generalised model for representing hybrids of relational-categorical, symbolic, perceptual-sensory and perceptual-latent data, so as to embody, in the same architectural data layer, representations for the input, output and latent tensors. This variety of representation is currently used by various machine-learning models in computer vision, NLP/NLU, reinforcement learning which allows for direct application of cross-domain queries and functions. This is achieved by endowing a directed Tensor-Typed Multi-Graph with at least some edge attributes which represent the embeddings from various latent spaces, so as to define, construct and compute new similarity and distance relationships between and across tensorial forms, including visual, linguistic, auditory latent representations, thus stitching the logical-categorical view of the observed universe to the Bayesian/statistical view.
