Cargando…
Interpretable Neuron Structuring with Graph Spectral Regularization
While neural networks are powerful approximators used to classify or embed data into lower dimensional spaces, they are often regarded as black boxes with uninterpretable features. Here we propose Graph Spectral Regularization for making hidden layers more interpretable without significantly impacti...
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
2020
|
| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8201816/ https://www.ncbi.nlm.nih.gov/pubmed/34131660 http://dx.doi.org/10.1007/978-3-030-44584-3_40 |