Stephan Günnemann

research

∙ 05/30/2023

MAGNet: Motif-Agnostic Generation of Molecules from Shapes

Recent advances in machine learning for molecules exhibit great potentia...

0 Leon Hetzel, et al. ∙

research

∙ 05/17/2023

Edge Directionality Improves Learning on Heterophilic Graphs

Graph Neural Networks (GNNs) have become the de-facto standard tool for ...

0 Emanuele Rossi, et al. ∙

research

∙ 05/01/2023

Revisiting Robustness in Graph Machine Learning

Many works show that node-level predictions of Graph Neural Networks (GN...

0 Lukas Gosch, et al. ∙

research

∙ 04/30/2023

Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness

Emerging quantum computing technologies, such as Noisy Intermediate-Scal...

0 Nicola Franco, et al. ∙

research

∙ 04/06/2023

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

Bayesian inference in deep neural networks is challenging due to the hig...

0 Jonas Gregor Wiese, et al. ∙

research

∙ 04/03/2023

Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Models

Modern machine learning models have started to consume incredible amount...

0 Johannes Getzner, et al. ∙

research

∙ 03/10/2023

Training, Architecture, and Prior for Deterministic Uncertainty Methods

Accurate and efficient uncertainty estimation is crucial to build reliab...

0 Bertrand Charpentier, et al. ∙

research

∙ 03/08/2023

Ewald-based Long-Range Message Passing for Molecular Graphs

Neural architectures that learn potential energy surfaces from molecular...

0 Arthur Kosmala, et al. ∙

research

∙ 02/08/2023

Generalizing Neural Wave Functions

Recent neural network-based wave functions have achieved state-of-the-ar...

0 Nicholas Gao, et al. ∙

research

∙ 02/06/2023

Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks

In tasks like node classification, image segmentation, and named-entity ...

0 Jan Schuchardt, et al. ∙

research

∙ 01/31/2023

Transformers Meet Directed Graphs

Transformers were originally proposed as a sequence-to-sequence model fo...

0 Simon Geisler, et al. ∙

research

∙ 01/31/2023

Are Defenses for Graph Neural Networks Robust?

A cursory reading of the literature suggests that we have made a lot of ...

0 Felix Mujkanovic, et al. ∙

research

∙ 01/05/2023

Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks

Randomized smoothing is one of the most promising frameworks for certify...

0 Yan Scholten, et al. ∙

research

∙ 01/02/2023

Training Differentially Private Graph Neural Networks with Random Walk Sampling

Deep learning models are known to put the privacy of their training data...

0 Morgane Ayle, et al. ∙

research

∙ 12/18/2022

Influence-Based Mini-Batching for Graph Neural Networks

Using graph neural networks for large graphs is challenging since there ...

0 Johannes Gasteiger, et al. ∙

research

∙ 11/25/2022

Invariance-Aware Randomized Smoothing Certificates

Building models that comply with the invariances inherent to different d...

0 Jan Schuchardt, et al. ∙

research

∙ 11/04/2022

Modeling Temporal Data as Continuous Functions with Process Diffusion

Temporal data like time series are often observed at irregular intervals...

0 Marin Biloš, et al. ∙

research

∙ 10/28/2022

Localized Randomized Smoothing for Collective Robustness Certification

Models for image segmentation, node classification and many other tasks ...

0 Jan Schuchardt, et al. ∙

research

∙ 10/22/2022

torchode: A Parallel ODE Solver for PyTorch

We introduce an ODE solver for the PyTorch ecosystem that can solve mult...

0 Marten Lienen, et al. ∙

research

∙ 10/19/2022

Irregularly-Sampled Time Series Modeling with Spline Networks

Observations made in continuous time are often irregular and contain the...

0 Marin Biloš, et al. ∙

research

∙ 10/15/2022

Unveiling the Sampling Density in Non-Uniform Geometric Graphs

A powerful framework for studying graphs is to consider them as geometri...

0 Raffaele Paolino, et al. ∙

research

∙ 09/16/2022

A Systematic Evaluation of Node Embedding Robustness

Node embedding methods map network nodes to low dimensional vectors that...

0 Alexandru Mara, et al. ∙

research

∙ 09/15/2022

MDE for Machine Learning-Enabled Software Systems: A Case Study and Comparison of MontiAnna ML-Quadrat

In this paper, we propose to adopt the MDE paradigm for the development ...

0 Jörg Christian Kirchhof, et al. ∙

research

∙ 07/17/2022

United States Politicians' Tone Became More Negative with 2016 Primary Campaigns

There is a widespread belief that the tone of US political language has ...

0 Jonathan Külz, et al. ∙

research

∙ 07/09/2022

On the Robustness and Anomaly Detection of Sparse Neural Networks

The robustness and anomaly detection capability of neural networks are c...

0 Morgane Ayle, et al. ∙

research

∙ 06/21/2022

Winning the Lottery Ahead of Time: Efficient Early Network Pruning

Pruning, the task of sparsifying deep neural networks, received increasi...

0 John Rachwan, et al. ∙

research

∙ 06/03/2022

Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning

Characterizing aleatoric and epistemic uncertainty on the predicted rewa...

0 Bertrand Charpentier, et al. ∙

research

∙ 05/30/2022

Sampling-free Inference for Ab-Initio Potential Energy Surface Networks

Obtaining the energy of molecular systems typically requires solving the...

3 Nicholas Gao, et al. ∙

research

∙ 04/28/2022

Predicting single-cell perturbation responses for unseen drugs

Single-cell transcriptomics enabled the study of cellular heterogeneity ...

0 Leon Hetzel, et al. ∙

research

∙ 04/06/2022

How Do Graph Networks Generalize to Large and Diverse Molecular Systems?

The predominant method of demonstrating progress of atomic graph neural ...

78 Johannes Gasteiger, et al. ∙

research

∙ 03/16/2022

Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks

We propose a new method for spatio-temporal forecasting on arbitrarily d...

0 Marten Lienen, et al. ∙

research

∙ 03/16/2022

Is it all a cluster game? – Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space

It is essential for safety-critical applications of deep neural networks...

16 Poulami Sinhamahapatra, et al. ∙

research

∙ 03/16/2022

Differentiable DAG Sampling

We propose a new differentiable probabilistic model over DAGs (DP-DAG). ...

0 Bertrand Charpentier, et al. ∙

research

∙ 03/06/2022

Enabling Automated Machine Learning for Model-Driven AI Engineering

Developing smart software services requires both Software Engineering an...

0 Armin Moin, et al. ∙

research

∙ 02/17/2022

Graph Data Augmentation for Graph Machine Learning: A Survey

Data augmentation has recently seen increased interest in graph machine ...

0 Tong Zhao, et al. ∙

research

∙ 02/17/2022

Multi-Objective Model Selection for Time Series Forecasting

Research on time series forecasting has predominantly focused on develop...

0 Oliver Borchert, et al. ∙

research

∙ 11/08/2021

Directional Message Passing on Molecular Graphs via Synthetic Coordinates

Graph neural networks that leverage coordinates via directional message ...

0 Johannes Klicpera, et al. ∙

research

∙ 10/26/2021

Robustness of Graph Neural Networks at Scale

Graph Neural Networks (GNNs) are increasingly important given their popu...

0 Simon Geisler, et al. ∙

research

∙ 10/26/2021

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

The interdependence between nodes in graphs is key to improve class pred...

0 Maximilian Stadler, et al. ∙

research

∙ 10/25/2021

Neural Flows: Efficient Alternative to Neural ODEs

Neural ordinary differential equations describe how values change in tim...

16 Marin Biloš, et al. ∙

research

∙ 10/21/2021

Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness

End-to-end (geometric) deep learning has seen first successes in approxi...

5 Simon Geisler, et al. ∙

research

∙ 10/11/2021

Intriguing Properties of Input-dependent Randomized Smoothing

Randomized smoothing is currently considered the state-of-the-art method...

0 Peter Súkeník, et al. ∙

research

∙ 10/11/2021

Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions

Solving the Schrödinger equation is key to many quantum mechanical prope...

0 Nicholas Gao, et al. ∙

research

∙ 09/10/2021

A Study of Joint Graph Inference and Forecasting

We study a recent class of models which uses graph neural networks (GNNs...

0 Daniel Zügner, et al. ∙

research

∙ 09/06/2021

On Second-order Optimization Methods for Federated Learning

We consider federated learning (FL), where the training data is distribu...

0 Sebastian Bischoff, et al. ∙

research

∙ 08/30/2021

Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)

Biological neural networks define the brain function and intelligence of...

38 Johannes C. Paetzold, et al. ∙

research

∙ 07/19/2021

OODformer: Out-Of-Distribution Detection Transformer

A serious problem in image classification is that a trained model might ...

0 Rajat Koner, et al. ∙

research

∙ 07/14/2021

Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More

The current best practice for computing optimal transport (OT) is via en...

0 Johannes Klicpera, et al. ∙

research

∙ 07/14/2021

MDE4QAI: Towards Model-Driven Engineering for Quantum Artificial Intelligence

Over the past decade, Artificial Intelligence (AI) has provided enormous...

0 Armin Moin, et al. ∙

research

∙ 07/13/2021

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering

Visual Question Answering (VQA) is concerned with answering free-form qu...

0 Rajat Koner, et al. ∙

Stephan Günnemann

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