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Equivariant Normalizing Flows for Point Processes and Sets

Oct 07, 2020
Marin Biloš, Stephan Günnemann


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ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning

Sep 22, 2020
Armin Moin, Stephan Rössler, Stephan Günnemann

* Published in Proc. of the International Conference on Model Driven Engineering Languages and Systems (MODELS) 2018 Workshops (MDE4IoT) 

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From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)

Sep 22, 2020
Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann

* International Conference on Model Driven Engineering Languages and Systems (MODELS) 2020 Poster Companion (Extended Abstract) 

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Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More

Aug 29, 2020
Aleksandar Bojchevski, Johannes Klicpera, Stephan Günnemann

* Proceedings of the 37th International Conference on Machine Learning (ICML 2020) 

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Reachable Sets of Classifiers & Regression Models: (Non-)Robustness Analysis and Robust Training

Jul 28, 2020
Anna-Kathrin Kopetzki, Stephan Günnemann

* 20 pages 

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Deep Representation Learning and Clustering of Traffic Scenarios

Jul 15, 2020
Nick Harmening, Marin Biloš, Stephan Günnemann

* Workshop on AI for Autonomous Driving, International Conference on Machine Learning (ICML) 2020 

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Scaling Graph Neural Networks with Approximate PageRank

Jul 03, 2020
Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, Stephan Günnemann

* Published as a Conference Paper at ACM SIGKDD 2020 

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Scene Graph Reasoning for Visual Question Answering

Jul 02, 2020
Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann

* ICML Workshop Graph Representation Learning and Beyond (GRL+) 

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Fast and Flexible Temporal Point Processes with Triangular Maps

Jun 22, 2020
Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann


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Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

Jun 16, 2020
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann


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Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs

Mar 31, 2020
Zhen Han, Yuyi Wang, Yunpu Ma, Stephan Günnemann, Volker Tresp

* Workshop on learning with temporal point processes, NeurIPS 2019 

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Directional Message Passing for Molecular Graphs

Mar 06, 2020
Johannes Klicpera, Janek Groß, Stephan Günnemann

* Published as a conference paper at ICLR 2020 

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Diffusion Improves Graph Learning

Dec 03, 2019
Johannes Klicpera, Stefan Weißenberger, Stephan Günnemann

* Thirty-third Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 2019 
* Published as a conference paper at NeurIPS 2019 

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Oktoberfest Food Dataset

Nov 22, 2019
Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann

* Dataset publication of Oktoberfest Food Dataset. 4 pages, 6 figures 

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Uncertainty on Asynchronous Time Event Prediction

Nov 13, 2019
Marin Biloš, Bertrand Charpentier, Stephan Günnemann

* Neurips 2019 (Spotlight) 

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Certifiable Robustness to Graph Perturbations

Oct 31, 2019
Aleksandar Bojchevski, Stephan Günnemann

* 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada 

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Overlapping Community Detection with Graph Neural Networks

Sep 26, 2019
Oleksandr Shchur, Stephan Günnemann

* The First International Workshop on Deep Learning on Graphs (In Conjunction with the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining) https://dlg2019.bitbucket.io/ 

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Intensity-Free Learning of Temporal Point Processes

Sep 26, 2019
Oleksandr Shchur, Marin Biloš, Stephan Günnemann


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Certifiable Robustness and Robust Training for Graph Convolutional Networks

Jun 28, 2019
Daniel Zügner, Stephan Günnemann

* Published as a Conference Paper at ACM SIGKDD 2019 

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Adversarial Attacks on Graph Neural Networks via Meta Learning

Feb 22, 2019
Daniel Zügner, Stephan Günnemann

* International Conference on Learning Representations (ICLR), New Orleans, LA, USA, 2019 
* ICLR submission 

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Multi-Source Neural Variational Inference

Nov 17, 2018
Richard Kurle, Stephan Günnemann, Patrick van der Smagt

* AAAI 2019, Association for the Advancement of Artificial Intelligence (AAAI) 2019 

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Pitfalls of Graph Neural Network Evaluation

Nov 14, 2018
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, Stephan Günnemann


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Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift

Oct 29, 2018
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton

* Submitted to the NIPS 2018 Workshop on Security in Machine Learning 

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Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank

Oct 14, 2018
Johannes Klicpera, Aleksandar Bojchevski, Stephan Günnemann


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Mining Contrasting Quasi-Clique Patterns

Oct 03, 2018
Roberto Alonso, Stephan Günnemann

* 10 pages 

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Adversarial Attacks on Node Embeddings

Sep 14, 2018
Aleksandar Bojchevski, Stephan Günnemann


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Adversarial Attacks on Neural Networks for Graph Data

Jun 12, 2018
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

* Accepted as a full paper at KDD 2018 on May 6, 2018 

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Dual-Primal Graph Convolutional Networks

Jun 03, 2018
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein


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NetGAN: Generating Graphs via Random Walks

Jun 01, 2018
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

* ICML 2018 

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