Zusammenfassung für den Vortrag am 09.06.2022 (17:00 Uhr)Math Machine Learning seminar MPI MIS + UCLA
Guang Cheng (UCLA)
Nonparametric Perspective on Deep Learning
Siehe auch das Video dieses Vortrages.
Models built with deep neural network (DNN) can handle
complicated real-world data extremely well, without suffering from the
curse of dimensionality or the non-convex optimization. To contribute to
the theoretical understanding of deep learning, we will investigate the
nonparametric aspects of DNNs by addressing the following questions: (i)
what kind of data can be best learned by deep neural networks？ (ii) can
deep neural networks achieve the statistical optimality? (iii) is there any
algorithmic guarantee for obtaining such optimal neural networks? Our
theoretical analysis applies to two most fundamental setup in practice:
regression and classification.