Stephan Günnemann

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Detecting Anomalous Event Sequences with Temporal Point Processes

Automatically detecting anomalies in event data can provide substantial ...
06/08/2021 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

Uncertainty awareness is crucial to develop reliable machine learning mo...
05/10/2021 ∙ by Bertrand Charpentier, et al. ∙ 0 ∙ share

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Neural Temporal Point Processes: A Review

Temporal point processes (TPP) are probabilistic generative models for c...
04/08/2021 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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Language-Agnostic Representation Learning of Source Code from Structure and Context

Source code (Context) and its parsed abstract syntax tree (AST; Structur...
03/21/2021 ∙ by Daniel Zügner, et al. ∙ 5 ∙ share

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Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules

Many important tasks in chemistry revolve around molecules during reacti...
11/28/2020 ∙ by Johannes Klicpera, et al. ∙ 0 ∙ share

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Reliable Graph Neural Networks via Robust Aggregation

Perturbations targeting the graph structure have proven to be extremely ...
10/29/2020 ∙ by Simon Geisler, et al. ∙ 0 ∙ share

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Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?

Robustness to adversarial perturbations and accurate uncertainty estimat...
10/28/2020 ∙ by Anna-Kathrin Kopetzki, et al. ∙ 11 ∙ share

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Equivariant Normalizing Flows for Point Processes and Sets

A point process describes how random sets of exchangeable points are gen...
10/07/2020 ∙ by Marin Biloš, et al. ∙ 0 ∙ share

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

In this paper, we present the current position of the research project M...
09/22/2020 ∙ by Armin Moin, et al. ∙ 0 ∙ share

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

In this paper, we illustrate how to enhance an existing state-of-the-art...
09/22/2020 ∙ by Armin Moin, et al. ∙ 0 ∙ share

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

Existing techniques for certifying the robustness of models for discrete...
08/29/2020 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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

Neural networks achieve outstanding accuracy in classification and regre...
07/28/2020 ∙ by Anna-Kathrin Kopetzki, et al. ∙ 0 ∙ share

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Deep Representation Learning and Clustering of Traffic Scenarios

Determining the traffic scenario space is a major challenge for the homo...
07/15/2020 ∙ by Nick Harmening, et al. ∙ 0 ∙ share

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Scaling Graph Neural Networks with Approximate PageRank

Graph neural networks (GNNs) have emerged as a powerful approach for sol...
07/03/2020 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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Scene Graph Reasoning for Visual Question Answering

Visual question answering is concerned with answering free-form question...
07/02/2020 ∙ by Marcel Hildebrandt, et al. ∙ 0 ∙ share

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Fast and Flexible Temporal Point Processes with Triangular Maps

Temporal point process (TPP) models combined with recurrent neural netwo...
06/22/2020 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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

Accurate estimation of aleatoric and epistemic uncertainty is crucial to...
06/16/2020 ∙ by Bertrand Charpentier, et al. ∙ 11 ∙ share

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Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs

The Hawkes process has become a standard method for modeling self-exciti...
03/30/2020 ∙ by Zhen Han, et al. ∙ 0 ∙ share

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Directional Message Passing for Molecular Graphs

Graph neural networks have recently achieved great successes in predicti...
03/06/2020 ∙ by Johannes Klicpera, et al. ∙ 0 ∙ share

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Uncertainty on Asynchronous Time Event Prediction

Asynchronous event sequences are the basis of many applications througho...
11/13/2019 ∙ by Marin Biloš, et al. ∙ 23 ∙ share

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Certifiable Robustness to Graph Perturbations

Despite the exploding interest in graph neural networks there has been l...
10/31/2019 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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Group Centrality Maximization for Large-scale Graphs

The study of vertex centrality measures is a key aspect of network analy...
10/30/2019 ∙ by Eugenio Angriman, et al. ∙ 0 ∙ share

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Diffusion Improves Graph Learning

Graph convolution is the core of most Graph Neural Networks (GNNs) and u...
10/28/2019 ∙ by Johannes Klicpera, et al. ∙ 0 ∙ share

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Overlapping Community Detection with Graph Neural Networks

Community detection is a fundamental problem in machine learning. While ...
09/26/2019 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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Intensity-Free Learning of Temporal Point Processes

Temporal point processes are the dominant paradigm for modeling sequence...
09/26/2019 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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Certifiable Robustness and Robust Training for Graph Convolutional Networks

Recent works show that Graph Neural Networks (GNNs) are highly non-robus...
06/28/2019 ∙ by Daniel Zügner, et al. ∙ 0 ∙ share

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Adversarial Attacks on Graph Neural Networks via Meta Learning

Deep learning models for graphs have advanced the state of the art on ma...
02/22/2019 ∙ by Daniel Zügner, et al. ∙ 0 ∙ share

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Pitfalls of Graph Neural Network Evaluation

Semi-supervised node classification in graphs is a fundamental problem i...
11/14/2018 ∙ by Oleksandr Shchur, et al. ∙ 0 ∙ share

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

We might hope that when faced with unexpected inputs, well-designed soft...
10/29/2018 ∙ by Stephan Rabanser, et al. ∙ 10 ∙ share

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

Neural message passing algorithms for semi-supervised classification on ...
10/14/2018 ∙ by Johannes Klicpera, et al. ∙ 0 ∙ share

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Mining Contrasting Quasi-Clique Patterns

Mining dense quasi-cliques is a well-known clustering task with applicat...
10/03/2018 ∙ by Roberto Alonso, et al. ∙ 0 ∙ share

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Adversarial Attacks on Node Embeddings

The goal of network representation learning is to learn low-dimensional ...
09/04/2018 ∙ by Aleksandar Bojcheski, et al. ∙ 0 ∙ share

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Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep l...
06/03/2018 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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Adversarial Attacks on Neural Networks for Graph Data

Deep learning models for graphs have achieved strong performance for the...
05/21/2018 ∙ by Daniel Zügner, et al. ∙ 0 ∙ share

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Adversarial Attacks on Classification Models for Graphs

Deep learning models for graphs have achieved strong performance for the...
05/21/2018 ∙ by Daniel Zügner, et al. ∙ 0 ∙ share

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NetGAN: Generating Graphs via Random Walks

We propose NetGAN - the first implicit generative model for graphs able ...
03/02/2018 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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Introduction to Tensor Decompositions and their Applications in Machine Learning

Tensors are multidimensional arrays of numerical values and therefore ge...
11/29/2017 ∙ by Stephan Rabanser, et al. ∙ 0 ∙ share

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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking

Methods that learn representations of graph nodes play a critical role i...
07/12/2017 ∙ by Aleksandar Bojchevski, et al. ∙ 0 ∙ share

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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Review fraud is a pervasive problem in online commerce, in which fraudul...
11/19/2015 ∙ by Bryan Hooi, et al. ∙ 0 ∙ share

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Linearized and Single-Pass Belief Propagation

How can we tell when accounts are fake or real in a social network? And ...
06/27/2014 ∙ by Wolfgang Gatterbauer, et al. ∙ 0 ∙ share

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Stephan Günnemann

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