Fundamental Limits of Learning with Feedforward and Recurrent Neural Networks

Add to your list(s) Download to your calendar using vCal

• Prof. Helmut Bolcskei, ETH Zurich
• Friday 27 May 2022, 12:00-13:00
• Department of Engineering - LT1.

If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

Deep neural networks have led to breakthrough results in numerous practical machine learning tasks such as image classification, image captioning, control-policy-learning to play the board game Go, and most recently the prediction of protein structures. In this lecture, we will attempt to understand some of the structural and mathematical reasons driving these successes. Specifically, we study what is possible in principle if no constraints are imposed on the learning algorithm and on the amount and quality of training data. The guiding theme will be a relation between the complexity of the objects to be learned and the networks approximating them, with the central result stating that universal Kolmogorov-optimality is achieved by feedforward neural networks in function learning and by recurrent neural networks in dynamical system learning.

This talk is part of the Information Engineering Distinguished Lecture Series series.

This talk is included in these lists:

• All Talks (aka the CURE list)
• Cambridge Centre for Data-Driven Discovery (C2D3)
• Cambridge University Engineering Department Talks
• Cambridge talks
• Centre for Smart Infrastructure & Construction
• Chris Davis' list
• Computational Continuum Mechanics Group Seminars
• Department of Engineering - LT1
• Department of Engineering - LT1 - please note room change
• Featured lists
• Information Engineering Distinguished Lecture Series
• Information Engineering Division seminar list
• Interested Talks
• School of Technology
• Signal Processing and Communications Lab Seminars
• Trust & Technology Initiative - interesting events
• bld31
• ndk22's list
• ob366-ai4er
• rp587

Note that ex-directory lists are not shown.