Search Results for author: Nico Goernitz

Found 5 papers, 2 papers with code

Deep One-Class Classification

1 code implementation ICML 2018 Lukas Ruff, Robert Vandermeulen, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection.

Anomaly Detection Classification +2

SynsetRank: Degree-adjusted Random Walk for Relation Identification

no code implementations2 Sep 2016 Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Goernitz, Feiyu Xu

In relation extraction, a key process is to obtain good detectors that find relevant sentences describing the target relation.

Relation Extraction

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 +2

mTim: Rapid and accurate transcript reconstruction from RNA-Seq data

1 code implementation20 Sep 2013 Georg Zeller, Nico Goernitz, Andre Kahles, Jonas Behr, Pramod Mudrakarta, Soeren Sonnenburg, Gunnar Raetsch

Recent advances in high-throughput cDNA sequencing (RNA-Seq) technology have revolutionized transcriptome studies.

Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation

no code implementations NeurIPS 2011 Nico Goernitz, Christian Widmer, Georg Zeller, Andre Kahles, Gunnar Rätsch, Sören Sonnenburg

We present a novel regularization-based Multitask Learning (MTL) formulation for Structured Output (SO) prediction for the case of hierarchical task relations.

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