Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces

We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this model-driven approach may require less training data and can potentially be more generalizable as it shows greater resilience to random attacks...

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Detalles Bibliográficos
Autores principales: Hernández-Orozco, Santiago, Zenil, Hector, Riedel, Jürgen, Uccello, Adam, Kiani, Narsis A., Tegnér, Jesper
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Artificial Intelligence
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944352/
https://www.ncbi.nlm.nih.gov/pubmed/33733213
http://dx.doi.org/10.3389/frai.2020.567356

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7944352/
https://www.ncbi.nlm.nih.gov/pubmed/33733213
http://dx.doi.org/10.3389/frai.2020.567356

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