In Term 1 we plan to cover Functional Approximators (NN) & Dimensionality reduction methods.

For this section we mainly follow the book "Deep Learning architectures. A mathematical approach" by Ovidiu Calin 

We meet every Wednesday 11:00-12:00 in B3.03 (Zeeman).

To get the latest updates, sign up to the mailing list.

Date	Topic	Paper/Text book chapters	Presenter
12/10/22	Organisational meeting
19/10/22	Introduction to Neural Networks	"Deep Learning architectures. A mathematical approach" by Ovidiu Calin, Ch. 1, 5, 6	Claudia Viaro
26/10/22	NN and approximation theorems	"Deep Learning architectures. A mathematical approach" by Ovidiu Calin, Ch. 6, 7	Anna Kuchko
02/11/22	Universal approximation theorems	"Deep Learning architectures. A mathematical approach" by Ovidiu Calin, Ch. 9	Laszlo Udvardi
09/11/22	Compression techniques and neural manifolds	"Deep Learning architectures. A mathematical approach" by Ovidiu Calin, Ch. 13,14	Anna Kuchko
16/11/22	Recurrent Neural Networks	"Deep Learning architectures. A mathematical approach" by Ovidiu Calin, Ch. 17	Pablo Ramses Alonso Martin
	Mid-term pub
23/11/22	Graph Neural Networks	Genaral theory+recent papers	Richard Fox
30/11/22	Physics-Informed Deep Learning	"Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations" by M. Raissi, P. Perdikaris, G. E. Karniadakis	Claudia Viaro
7/12/22	Reservoir Computing & Echo State Networks	"Echo state networks are universal" by L. Grigoryeva and J.-P. Ortega	Guglielmo Gattiglio

In AY 2022-2023 the reading group is organised by Claudia Viaro, Anna Kuchko and Laszlo Udvardi. Please don't hesitate to contact us if you have any questions or suggestions!