Media Summary: Abstract: Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex ... Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex tasks. 10:00- 11:00 Ruthotto, Lars Numerical Methods for Deep Learning 11:00- ...

Philipp Petersen Functions With Structured - Detailed Analysis & Overview

Abstract: Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex ... Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex tasks. 10:00- 11:00 Ruthotto, Lars Numerical Methods for Deep Learning 11:00- ... Title: High-dimensional Classification with Deep Neural Networks Abstract: We discuss classification problems in high-dimensions ... we discuss classification problems in high dimension. We study classification problems using three classical notions: complexity ... A talk given during the Principles of Intelligence (PIBBSS) workshop in Boston in October 2025.

Speaker: Helmut BÖLCSKEI (ETH Zürich) Workshop on Science of Data Science (smr 3283) 2019_10_01-09_45-smr3283.mp4.

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Philipp Petersen, Functions with structured singularities, 2020.11.17
Optimal Representation and Learning of Classifier Functions- Philipp Petersen
1W-MINDS: Philipp Petersen, December 9, 2021, Optimal learning of classifier functions
Mathematical and Computational Aspects of Machine Learning - 11 October 2019
UQ Hybrid Seminar - Prof. Philipp Petersen, University of Vienna
Philipp Petersen: High-dimensional classification with deeep neural networks: decision boundaries
Philipp Alexander Kreer - Bayesian Influence Functions
Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions
Fundamental limits of deep neural network learning
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Philipp Petersen, Functions with structured singularities, 2020.11.17

Philipp Petersen, Functions with structured singularities, 2020.11.17

Speaker:

Optimal Representation and Learning of Classifier Functions- Philipp Petersen

Optimal Representation and Learning of Classifier Functions- Philipp Petersen

Abstract: Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex ...

1W-MINDS: Philipp Petersen, December 9, 2021, Optimal learning of classifier functions

1W-MINDS: Philipp Petersen, December 9, 2021, Optimal learning of classifier functions

Deep learning has established itself as, by far, the most successful machine learning approach in sufficiently complex tasks.

Mathematical and Computational Aspects of Machine Learning - 11 October 2019

Mathematical and Computational Aspects of Machine Learning - 11 October 2019

http://www.crm.sns.it/event/451/timetable.html#title 10:00- 11:00 Ruthotto, Lars Numerical Methods for Deep Learning 11:00- ...

UQ Hybrid Seminar - Prof. Philipp Petersen, University of Vienna

UQ Hybrid Seminar - Prof. Philipp Petersen, University of Vienna

Title: High-dimensional Classification with Deep Neural Networks Abstract: We discuss classification problems in high-dimensions ...

Philipp Petersen: High-dimensional classification with deeep neural networks: decision boundaries

Philipp Petersen: High-dimensional classification with deeep neural networks: decision boundaries

we discuss classification problems in high dimension. We study classification problems using three classical notions: complexity ...

Philipp Alexander Kreer - Bayesian Influence Functions

Philipp Alexander Kreer - Bayesian Influence Functions

A talk given during the Principles of Intelligence (PIBBSS) workshop in Boston in October 2025.

Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions

Deep Learning(CS7015): Lec 4.3 Output functions and Loss functions

lec04mod03.

Fundamental limits of deep neural network learning

Fundamental limits of deep neural network learning

Speaker: Helmut BÖLCSKEI (ETH Zürich) Workshop on Science of Data Science | (smr 3283) 2019_10_01-09_45-smr3283.mp4.