Cargando…
Less is more: regularization perspectives on large scale machine learning
<!--HTML--><p>Modern data-sets are often huge, possibly high-dimensional, and require complex non-linear parameterization to be modeled accurately.<br /> Examples include image and audio classification but also data analysis problems in natural sciences, e..g high energy physics or...
Autor principal: | Rosasco, Lorenzo |
---|---|
Lenguaje: | eng |
Publicado: |
2017
|
Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2269969 |
Ejemplares similares
-
Learning to discover: machine learning in high-energy physics
por: Kégl, Balázs
Publicado: (2014) -
Machine Learning applications in CMS
por: Stoye, Markus
Publicado: (2017) -
Tracking at Hadron Colliders with Machine Learning
por: Vlimant, Jean-Roch
Publicado: (2019) -
Real-time Machine Learning in particle physics
por: Aarrestad, Thea
Publicado: (2022) -
How the machine learning conquers reconstruction in neutrino experiments
por: Sulej, Robert
Publicado: (2017)