The impact of machine learning (ML) in many fields of application is constrained by lack of annotated data. Among existing tools for ML-assisted data annotation, one little explored tool type relies on an analogy between the coordinates of a graphical user interface and the latent space of a neural network for interaction through direct manipulation. In the present work, we 1) expand the paradigm by proposing two new analogies: time and force as reflecting iterations and gradients of network training; 2) propose a network model for learning a compact graphical representation of the data that takes into account both its internal structure and user provided annotations; and 3) investigate the impact of model hyperparameters on the learned graphical representations of the data, identifying candidate model variants for a future user study.

翻译：机器学习（ML）在许多应用领域中的影响力受到标注数据匮乏的制约。在现有的ML辅助数据标注工具中，有一类鲜少被探索的工具类型依赖于图形用户界面坐标与神经网络潜在空间之间的类比关系，通过直接操控实现交互。在本工作中，我们：1）拓展了这一范式，提出两种新的类比关系：将时间与网络训练的迭代过程类比，将作用力与梯度类比；2）提出一种网络模型，用于学习数据的紧凑图形表征，该表征同时兼顾数据内部结构及用户提供的标注信息；3）探究模型超参数对数据图形表征学习效果的影响，确定未来用户研究中候选模型变体的方案。