382 citations · 437 across the 6 of their papers we have counts for
7 papers
Deep Semi-Supervised Anomaly Detection
Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz +4
Deep approaches to anomaly detection have recently shown promising results over shallow methods on large and complex datasets. Typically anomaly detection is treated as an unsuperv…
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Jacob Kauffmann, Grégoire Montavon, Luiz Alberto Lima +3
Detecting and explaining anomalies is a challenging effort. This holds especially true when data exhibits strong dependencies and single measurements need to be assessed and analyz…
Optimizing for Measure of Performance in Max-Margin Parsing
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Many statistical learning problems in the area of natural language processing including sequence tagging, sequence segmentation and syntactic parsing has been successfully approach…
Partial Optimality of Dual Decomposition for MAP Inference in Pairwise MRFs
Alexander Bauer, Shinichi Nakajima, Nico Görnitz +1
Markov random fields (MRFs) are a powerful tool for modelling statistical dependencies for a set of random variables using a graphical representation. An important computational pr…
Feature Importance Measure for Non-linear Learning Algorithms
Marina M. -C. Vidovic, Nico Görnitz, Klaus-Robert Müller +1
Complex problems may require sophisticated, non-linear learning methods such as kernel machines or deep neural networks to achieve state of the art prediction accuracies. However,…
SynsetRank: Degree-adjusted Random Walk for Relation Identification
Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn +3
In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation. To minimize the necessity of labeled data for refinin…
Toward Supervised Anomaly Detection
Nico Goernitz, Marius Micha Kloft, Konrad Rieck +1
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive perfo…