
Detecting Anomalous Event Sequences with Temporal Point Processes
Automatically detecting anomalies in event data can provide substantial ...
read it

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
Uncertainty awareness is crucial to develop reliable machine learning mo...
read it

Neural Temporal Point Processes: A Review
Temporal point processes (TPP) are probabilistic generative models for c...
read it

LanguageAgnostic Representation Learning of Source Code from Structure and Context
Source code (Context) and its parsed abstract syntax tree (AST; Structur...
read it

Fast and UncertaintyAware Directional Message Passing for NonEquilibrium Molecules
Many important tasks in chemistry revolve around molecules during reacti...
read it

Reliable Graph Neural Networks via Robust Aggregation
Perturbations targeting the graph structure have proven to be extremely ...
read it

Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichletbased Models Reliable?
Robustness to adversarial perturbations and accurate uncertainty estimat...
read it

Equivariant Normalizing Flows for Point Processes and Sets
A point process describes how random sets of exchangeable points are gen...
read it

ThingML+ Augmenting ModelDriven Software Engineering for the Internet of Things with Machine Learning
In this paper, we present the current position of the research project M...
read it

From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
In this paper, we illustrate how to enhance an existing stateoftheart...
read it

Efficient Robustness Certificates for Discrete Data: SparsityAware Randomized Smoothing for Graphs, Images and More
Existing techniques for certifying the robustness of models for discrete...
read it

Reachable Sets of Classifiers Regression Models: (Non)Robustness Analysis and Robust Training
Neural networks achieve outstanding accuracy in classification and regre...
read it

Deep Representation Learning and Clustering of Traffic Scenarios
Determining the traffic scenario space is a major challenge for the homo...
read it

Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
read it

Scene Graph Reasoning for Visual Question Answering
Visual question answering is concerned with answering freeform question...
read it

Fast and Flexible Temporal Point Processes with Triangular Maps
Temporal point process (TPP) models combined with recurrent neural netwo...
read it

Posterior Network: Uncertainty Estimation without OOD Samples via DensityBased PseudoCounts
Accurate estimation of aleatoric and epistemic uncertainty is crucial to...
read it

Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling selfexciti...
read it

Directional Message Passing for Molecular Graphs
Graph neural networks have recently achieved great successes in predicti...
read it

Uncertainty on Asynchronous Time Event Prediction
Asynchronous event sequences are the basis of many applications througho...
read it

Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been l...
read it

Group Centrality Maximization for Largescale Graphs
The study of vertex centrality measures is a key aspect of network analy...
read it

Diffusion Improves Graph Learning
Graph convolution is the core of most Graph Neural Networks (GNNs) and u...
read it

Overlapping Community Detection with Graph Neural Networks
Community detection is a fundamental problem in machine learning. While ...
read it

IntensityFree Learning of Temporal Point Processes
Temporal point processes are the dominant paradigm for modeling sequence...
read it

Certifiable Robustness and Robust Training for Graph Convolutional Networks
Recent works show that Graph Neural Networks (GNNs) are highly nonrobus...
read it

Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on ma...
read it

Pitfalls of Graph Neural Network Evaluation
Semisupervised node classification in graphs is a fundamental problem i...
read it

Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, welldesigned soft...
read it

Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank
Neural message passing algorithms for semisupervised classification on ...
read it

Mining Contrasting QuasiClique Patterns
Mining dense quasicliques is a wellknown clustering task with applicat...
read it

Adversarial Attacks on Node Embeddings
The goal of network representation learning is to learn lowdimensional ...
read it

DualPrimal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep l...
read it

Adversarial Attacks on Neural Networks for Graph Data
Deep learning models for graphs have achieved strong performance for the...
read it

Adversarial Attacks on Classification Models for Graphs
Deep learning models for graphs have achieved strong performance for the...
read it

NetGAN: Generating Graphs via Random Walks
We propose NetGAN  the first implicit generative model for graphs able ...
read it

Introduction to Tensor Decompositions and their Applications in Machine Learning
Tensors are multidimensional arrays of numerical values and therefore ge...
read it

Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Methods that learn representations of graph nodes play a critical role i...
read it

BIRDNEST: Bayesian Inference for RatingsFraud Detection
Review fraud is a pervasive problem in online commerce, in which fraudul...
read it

Linearized and SinglePass Belief Propagation
How can we tell when accounts are fake or real in a social network? And ...
read it
Stephan Günnemann
is this you? claim profile