28 citations · 33 across the 6 of their papers we have counts for
6 papers
GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators
Ousmane Touat, Julian Stier, Pierre-Edouard Portier +1
A wide variety of generative models for graphs have been proposed. They are used in drug discovery, road networks, neural architecture search, and program synthesis. Generating gra…
Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping
Sahib Julka, Michael Granitzer
Planetary science research involves analysing vast amounts of remote sensing data, which are often costly and time-consuming to annotate and process. One of the essential tasks in…
German BERT Model for Legal Named Entity Recognition
Harshil Darji, Jelena Mitrović, Michael Granitzer
The use of BERT, one of the most popular language models, has led to improvements in many Natural Language Processing (NLP) tasks. One such task is Named Entity Recognition (NER) i…
deepstruct -- linking deep learning and graph theory
Julian Stier, Michael Granitzer
deepstruct connects deep learning models and graph theory such that different graph structures can be imposed on neural networks or graph structures can be extracted from trained n…
Recommending Scientific Literature: Comparing Use-Cases and Algorithms
Roman Kern, Kris Jack, Michael Granitzer
An important aspect of a researcher's activities is to find relevant and related publications. The task of a recommender system for scientific publications is to provide a list of…
Assessing the Quality of Web Content
Elisabeth Lex, Inayat Khan, Horst Bischof +1
This paper describes our approach towards the ECML/PKDD Discovery Challenge 2010. The challenge consists of three tasks: (1) a Web genre and facet classification task for English h…