Sophie Langer - Circumventing the curse of dimensionality with deep neural networks
Presentation given by Sophie Langer on 12th of October 2022 in the one world seminar on the mathematics of machine learning on the topic "Circumventing the curse of dimensionality with deep neural networks ".
Abstract: Although the application of deep neural networks to real-world problems has become ubiquitous, the question of why they are so effective has not yet been satisfactorily answered. However, some progress has been made in establishing an underlying mathematical foundation. This talk surveys results on statistical risk bounds of deep neural networks. In particular, we focus on the question of when neural networks bypass the curse of dimensionality. Here we discuss results for vanilla feedforward and convolutional neural networks as well as regression and classification settings. This talk is based on several joint works with Alina Braun, Michael Kohler, Adam Krzyzak, Johannes Schmidt-Hieber and Harro Walk.
Видео Sophie Langer - Circumventing the curse of dimensionality with deep neural networks канала One world theoretical machine learning
Abstract: Although the application of deep neural networks to real-world problems has become ubiquitous, the question of why they are so effective has not yet been satisfactorily answered. However, some progress has been made in establishing an underlying mathematical foundation. This talk surveys results on statistical risk bounds of deep neural networks. In particular, we focus on the question of when neural networks bypass the curse of dimensionality. Here we discuss results for vanilla feedforward and convolutional neural networks as well as regression and classification settings. This talk is based on several joint works with Alina Braun, Michael Kohler, Adam Krzyzak, Johannes Schmidt-Hieber and Harro Walk.
Видео Sophie Langer - Circumventing the curse of dimensionality with deep neural networks канала One world theoretical machine learning
Показать
Комментарии отсутствуют
Информация о видео
19 октября 2022 г. 19:35:14
01:03:53
Другие видео канала
Lukasz Szpruch - Mean-Field Neural ODEs, Relaxed Control and Generalization Errors
Matthew Colbrook - Smale’s 18th Problem and the Barriers of Deep Learning
Yu Bai - How Important is the Train-Validation Split in Meta-Learning?
Anna Korba - Kernel Stein Discrepancy Descent
Anirbit Mukherjee - Provable Training of Neural Nets With One Layer of Activation
Kevin Miller - Ensuring Exploration and Exploitation in Graph-Based Active Learning
Theo Bourdais - Computational Hypergraph Discovery, a Gaussian Process framework
Yaoqing Yang - Predicting & improving generalization by measuring loss landscapes & weight matrices
Konstantinos Spiliopoulos - Mean field limits of neural networks: typical behavior and fluctuations
Nadia Drenska - A PDE Interpretation of Prediction with Expert Advice
Matthias Ehrhardt - Bilevel Learning for Inverse Problems
Peter Richtarik - The Resolution of a Question Related to Local Training in Federated Learning
Marcus Hutter - Testing Independence of Exchangeable Random Variables
Yury Korolev - Approximation properties of two-layer neural networks with values in a Banach space
Stephan Mandt - Compressing Variational Bayes: From neural data compression to video prediction
Derek Driggs - Barriers to Deploying Deep Learning Models During the COVID-19 Pandemic
Gal Vardi - Implications of the implicit bias in neural networks
Ziwei Ji - The dual of the margin: improved analyses and rates for gradient descent’s implicit bias
Qi Lei - Predicting What You Already Know Helps: Provable Self-Supervised Learning
Alessandro Scagliotti - Deep Learning Approximation of Diffeomorphisms via Linear-Control Systems