4 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
A Formally Robust Time Series Distance Metric
Maximilian Toller, Bernhard C. Geiger, Roman Kern
Distance-based classification is among the most competitive classification methods for time series data. The most critical component of distance-based classification is the selecte…
Class-Conditional Compression and Disentanglement: Bridging the Gap between Neural Networks and Naive Bayes Classifiers
Rana Ali Amjad, Bernhard C. Geiger
In this draft, which reports on work in progress, we 1) adapt the information bottleneck functional by replacing the compression term by class-conditional compression, 2) relax thi…
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
Marek Śmieja, Maciej Wołczyk, Jacek Tabor +1
We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto…
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle
Rana Ali Amjad, Bernhard C. Geiger
In this theory paper, we investigate training deep neural networks (DNNs) for classification via minimizing the information bottleneck (IB) functional. We show that the resulting o…
Semi-supervised cross-entropy clustering with information bottleneck constraint
Marek Śmieja, Bernhard C. Geiger
In this paper, we propose a semi-supervised clustering method, CEC-IB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling…