
Fast and UncertaintyAware Directional Message Passing for NonEquilibrium Molecules
Many important tasks in chemistry revolve around molecules during reacti...
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Reliable Graph Neural Networks via Robust Aggregation
Perturbations targeting the graph structure have proven to be extremely ...
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Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichletbased Models Reliable?
Robustness to adversarial perturbations and accurate uncertainty estimat...
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Equivariant Normalizing Flows for Point Processes and Sets
A point process describes how random sets of exchangeable points are gen...
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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...
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From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
In this paper, we illustrate how to enhance an existing stateoftheart...
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Efficient Robustness Certificates for Discrete Data: SparsityAware Randomized Smoothing for Graphs, Images and More
Existing techniques for certifying the robustness of models for discrete...
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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...
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Deep Representation Learning and Clustering of Traffic Scenarios
Determining the traffic scenario space is a major challenge for the homo...
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Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
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Scene Graph Reasoning for Visual Question Answering
Visual question answering is concerned with answering freeform question...
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Fast and Flexible Temporal Point Processes with Triangular Maps
Temporal point process (TPP) models combined with recurrent neural netwo...
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Posterior Network: Uncertainty Estimation without OOD Samples via DensityBased PseudoCounts
Accurate estimation of aleatoric and epistemic uncertainty is crucial to...
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Graph Hawkes Network for Reasoning on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling selfexciti...
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Directional Message Passing for Molecular Graphs
Graph neural networks have recently achieved great successes in predicti...
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Uncertainty on Asynchronous Time Event Prediction
Asynchronous event sequences are the basis of many applications througho...
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Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been l...
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Group Centrality Maximization for Largescale Graphs
The study of vertex centrality measures is a key aspect of network analy...
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Diffusion Improves Graph Learning
Graph convolution is the core of most Graph Neural Networks (GNNs) and u...
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Overlapping Community Detection with Graph Neural Networks
Community detection is a fundamental problem in machine learning. While ...
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IntensityFree Learning of Temporal Point Processes
Temporal point processes are the dominant paradigm for modeling sequence...
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Certifiable Robustness and Robust Training for Graph Convolutional Networks
Recent works show that Graph Neural Networks (GNNs) are highly nonrobus...
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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...
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Pitfalls of Graph Neural Network Evaluation
Semisupervised node classification in graphs is a fundamental problem i...
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Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
We might hope that when faced with unexpected inputs, welldesigned soft...
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Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank
Neural message passing algorithms for semisupervised classification on ...
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Mining Contrasting QuasiClique Patterns
Mining dense quasicliques is a wellknown clustering task with applicat...
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Adversarial Attacks on Node Embeddings
The goal of network representation learning is to learn lowdimensional ...
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DualPrimal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep l...
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Adversarial Attacks on Neural Networks for Graph Data
Deep learning models for graphs have achieved strong performance for the...
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Adversarial Attacks on Classification Models for Graphs
Deep learning models for graphs have achieved strong performance for the...
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NetGAN: Generating Graphs via Random Walks
We propose NetGAN  the first implicit generative model for graphs able ...
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Introduction to Tensor Decompositions and their Applications in Machine Learning
Tensors are multidimensional arrays of numerical values and therefore ge...
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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...
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BIRDNEST: Bayesian Inference for RatingsFraud Detection
Review fraud is a pervasive problem in online commerce, in which fraudul...
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Linearized and SinglePass Belief Propagation
How can we tell when accounts are fake or real in a social network? And ...
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Stephan Günnemann
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