1k citations · 4.2k across the 120 of their papers we have counts for
12 papers · 1 filter
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann +2
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if…
Invariance-Aware Randomized Smoothing Certificates
Jan Schuchardt, Stephan Günnemann
Building models that comply with the invariances inherent to different domains, such as invariance under translation or rotation, is a key aspect of applying machine learning to re…
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Marin Biloš, Kashif Rasul, Anderson Schneider +2
Temporal data such as time series can be viewed as discretized measurements of the underlying function. To build a generative model for such data we have to model the stochastic pr…
Irregularly-Sampled Time Series Modeling with Spline Networks
Marin Biloš, Emanuel Ramneantu, Stephan Günnemann
Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using spli…
Localized Randomized Smoothing for Collective Robustness Certification
Jan Schuchardt, Tom Wollschläger, Aleksandar Bojchevski +1
Models for image segmentation, node classification and many other tasks map a single input to multiple labels. By perturbing this single shared input (e.g. the image) an adversary…
torchode: A Parallel ODE Solver for PyTorch
Marten Lienen, Stephan Günnemann
We introduce an ODE solver for the PyTorch ecosystem that can solve multiple ODEs in parallel independently from each other while achieving significant performance gains. Our imple…
United States Politicians' Tone Became More Negative with 2016 Primary Campaigns
Jonathan Külz, Andreas Spitz, Ahmad Abu-Akel +2
There is a widespread belief that the tone of US political language has become more negative recently, in particular when Donald Trump entered politics. At the same time, there is…
Disentangling Epistemic and Aleatoric Uncertainty in Reinforcement Learning
Bertrand Charpentier, Ransalu Senanayake, Mykel Kochenderfer +1
Characterizing aleatoric and epistemic uncertainty on the predicted rewards can help in building reliable reinforcement learning (RL) systems. Aleatoric uncertainty results from th…
Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution
Leon Hetzel, Simon Böhm, Niki Kilbertus +3
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells. However, scaling high-throughput screens…
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
Marten Lienen, Stephan Günnemann
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we de…
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher +1
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution. In this paper, we…
Differentiable DAG Sampling
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG sa…