79 citations · 241 across the 27 of their papers we have counts for
8 papers · 1 filter
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras +20
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed usin…
HateBERT: Retraining BERT for Abusive Language Detection in English
Tommaso Caselli, Valerio Basile, Jelena Mitrović +1
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in…
ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level
Mathieu Garchery, Michael Granitzer
Previous works on the CERT insider threat detection case have neglected graph and text features despite their relevance to describe user behavior. Additionally, existing systems he…
DeepGG: a Deep Graph Generator
Julian Stier, Michael Granitzer
Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more. We present an improved framework for learning…
Investigating Extensions to Random Walk Based Graph Embedding
Joerg Schloetterer, Martin Wehking, Fatemeh Salehi Rizi +1
Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches. However, most of th…
Global and Local Feature Learning for Ego-Network Analysis
Fatemeh Salehi Rizi, Michael Granitzer, Konstantin Ziegler
In an ego-network, an individual (ego) organizes its friends (alters) in different groups (social circles). This social network can be efficiently analyzed after learning represent…
Predicting event attendance exploring social influence
Fatemeh Salehi Rizi, Michael Granitzer
The problem of predicting people's participation in real-world events has received considerable attention as it offers valuable insights for human behavior analysis and event-relat…
Shortest path distance approximation using deep learning techniques
Fatemeh Salehi Rizi, Joerg Schloetterer, Michael Granitzer
Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications. Traditional exact methods such as breadth-first-search (BFS) do not sca…