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Media Summary: Date: 10/15/25 Abstract: Graph Neural Networks (GNNs) have become a cornerstone for the application of Fall 21 - Computational Social Science Seminar - lecture 8 Computational Social Science Seminar held by prof. Dirk Helbing. Künstliche Intelligenz (KI) und Data Science sind die Schlüsselmethoden unseres Jahrhunderts. Hier werden Daten nicht nur ...

Ingo Scholtes On Deep Learning - Detailed Analysis & Overview

Date: 10/15/25 Abstract: Graph Neural Networks (GNNs) have become a cornerstone for the application of Fall 21 - Computational Social Science Seminar - lecture 8 Computational Social Science Seminar held by prof. Dirk Helbing. Künstliche Intelligenz (KI) und Data Science sind die Schlüsselmethoden unseres Jahrhunderts. Hier werden Daten nicht nur ... Visualization of a dynamic network using NETVisualizer, a software developed at the Chair of Systems Design, ETH Zurich. Joint work with Nathan Kutz: Discovering physical laws and ... pathpy is an OpenSource python package for the analysis of time series data on networks using higher- and multi-order network ...

Graph or network abstractions are an important foundation for the computational modeling of complex systems. They help us to ... One of the main challenges for AI remains unsupervised

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Ingo Scholtes on "Deep Learning for Temporal Graphs"
When is a network a network?
De Bruijn Goes Neural
Ingo Scholtes: From networks to optimal higher-order models of complex systems
There Will Be a Scientific Theory of Deep Learning
Neu an der Uni - Prof. Ingo Scholtes - Machine Learning for Complex Networks
Dynamic Network Visualization
Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning
Introducing pathpy 2.0
Ingo Scholtes "Understanding Complex Systems: From Networks to Multi-Order Models"
From Deep Learning of Disentangled Representations to Higher-level Cognition
Learning Theory 1 - Ingo Steinwart - MLSS 2013 Tübingen
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Ingo Scholtes on "Deep Learning for Temporal Graphs"

Ingo Scholtes on "Deep Learning for Temporal Graphs"

Date: 10/15/25 Abstract: Graph Neural Networks (GNNs) have become a cornerstone for the application of

When is a network a network?

When is a network a network?

Promotional video for paper:

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De Bruijn Goes Neural

De Bruijn Goes Neural

Temporal Graph

Ingo Scholtes: From networks to optimal higher-order models of complex systems

Ingo Scholtes: From networks to optimal higher-order models of complex systems

Fall 21 - Computational Social Science Seminar - lecture 8 Computational Social Science Seminar held by prof. Dirk Helbing.

There Will Be a Scientific Theory of Deep Learning

There Will Be a Scientific Theory of Deep Learning

Deep learning

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Neu an der Uni - Prof. Ingo Scholtes - Machine Learning for Complex Networks

Neu an der Uni - Prof. Ingo Scholtes - Machine Learning for Complex Networks

Künstliche Intelligenz (KI) und Data Science sind die Schlüsselmethoden unseres Jahrhunderts. Hier werden Daten nicht nur ...

Dynamic Network Visualization

Dynamic Network Visualization

Visualization of a dynamic network using NETVisualizer, a software developed at the Chair of Systems Design, ETH Zurich.

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Deep Learning to Discover Coordinates for Dynamics: Autoencoders & Physics Informed Machine Learning

Joint work with Nathan Kutz: https://www.youtube.com/channel/UCoUOaSVYkTV6W4uLvxvgiFA Discovering physical laws and ...

Introducing pathpy 2.0

Introducing pathpy 2.0

pathpy is an OpenSource python package for the analysis of time series data on networks using higher- and multi-order network ...

Ingo Scholtes "Understanding Complex Systems: From Networks to Multi-Order Models"

Ingo Scholtes "Understanding Complex Systems: From Networks to Multi-Order Models"

Graph or network abstractions are an important foundation for the computational modeling of complex systems. They help us to ...

From Deep Learning of Disentangled Representations to Higher-level Cognition

From Deep Learning of Disentangled Representations to Higher-level Cognition

One of the main challenges for AI remains unsupervised

Learning Theory 1 - Ingo Steinwart - MLSS 2013 Tübingen

Learning Theory 1 - Ingo Steinwart - MLSS 2013 Tübingen

This is

Is this still the best book on Machine Learning?

Is this still the best book on Machine Learning?

Hands on

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