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Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling
Niclas Dern, Lennart Redl, Sebastian Pfister +3
Sampling from unnormalized target distributions, e.g.\ Boltzmann distributions , is fundamental to many scientific applications yet comp…
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou +3
Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approach…
Multi-Source Neural Variational Inference
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference method…
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on prope…
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness t…
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner +1
We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased…
Introduction to Tensor Decompositions and their Applications in Machine Learning
Stephan Rabanser, Oleksandr Shchur, Stephan Günnemann
Tensors are multidimensional arrays of numerical values and therefore generalize matrices to multiple dimensions. While tensors first emerged in the psychometrics community in the…
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski, Stephan Günnemann
Methods that learn representations of nodes in a graph play a critical role in network analysis since they enable many downstream learning tasks. We propose Graph2Gauss - an approa…