Search Results for author: Ulf Brefeld

Found 11 papers, 2 papers with code

Joint Optimization of an Autoencoder for Clustering and Embedding

1 code implementation7 Dec 2020 Ahcène Boubekki, Michael Kampffmeyer, Robert Jenssen, Ulf Brefeld

That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding.

Deep Clustering

Principled Interpolation in Normalizing Flows

no code implementations22 Oct 2020 Samuel G. Fadel, Sebastian Mair, Ricardo da S. Torres, Ulf Brefeld

In this paper, we solve this issue by enforcing a fixed norm and, hence, change the base distribution, to allow for a principled way of interpolation.

Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?

no code implementations NeurIPS Workshop ICBINB 2020 Yannick Rudolph, Ulf Brefeld, Uwe Dick

Given a neural network with a graph architecture and/or structured output function, variational autoencoding does not seem to contribute statistically significantly to empirical performance.

Coresets for Archetypal Analysis

1 code implementation NeurIPS 2019 Sebastian Mair, Ulf Brefeld

Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data.

Quantization

Frame-based Data Factorizations

no code implementations ICML 2017 Sebastian Mair, Ahcène Boubekki, Ulf Brefeld

Archetypal Analysis is the method of choice to compute interpretable matrix factorizations.

Infinite Mixture Model of Markov Chains

no code implementations19 Jun 2017 Jan Reubold, Thorsten Strufe, Ulf Brefeld

We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme.

Time Series

Toward Supervised Anomaly Detection

no code implementations23 Jan 2014 Nico Goernitz, Marius Micha Kloft, Konrad Rieck, Ulf Brefeld

Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions.

Active Learning Network Intrusion Detection +3

Efficient and Accurate Lp-Norm Multiple Kernel Learning

no code implementations NeurIPS 2009 Marius Kloft, Ulf Brefeld, Pavel Laskov, Klaus-Robert Müller, Alexander Zien, Sören Sonnenburg

Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.

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