| Autor: |
Kath, Hannes, Gouvêa, Thiago S., Sonntag, Daniel |
| Rok vydání: |
2023 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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