79 citations · 241 across the 27 of their papers we have counts for
29 papers
Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings
Veronika Hackl, Alexandra Elena Müller, Michael Granitzer +1
This study investigates the consistency of feedback ratings generated by OpenAI's GPT-4, a state-of-the-art artificial intelligence language model, across multiple iterations, time…
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…
Technical Report: Impact of Position Bias on Language Models in Token Classification
Mehdi Ben Amor, Michael Granitzer, Jelena Mitrović
Language Models (LMs) have shown state-of-the-art performance in Natural Language Processing (NLP) tasks. Downstream tasks such as Named Entity Recognition (NER) or Part-of-Speech…
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…
SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump
Christofer Fellicious, Stewart Sentanoe, Michael Granitzer +1
Digital forensics is the process of extracting, preserving, and documenting evidence in digital devices. A commonly used method in digital forensics is to extract data from the mai…
The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer +1
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively…
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…
Experiments on Properties of Hidden Structures of Sparse Neural Networks
Julian Stier, Harshil Darji, Michael Granitzer
Sparsity in the structure of Neural Networks can lead to less energy consumption, less memory usage, faster computation times on convenient hardware, and automated machine learning…
Correlation Analysis between the Robustness of Sparse Neural Networks and their Random Hidden Structural Priors
M. Ben Amor, J. Stier, M. Granitzer
Deep learning models have been shown to be vulnerable to adversarial attacks. This perception led to analyzing deep learning models not only from the perspective of their performan…
Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation
Sahib Julka, Vishal Sowrirajan, Joerg Schloetterer +1
Motion behaviour is driven by several factors -- goals, presence and actions of neighbouring agents, social relations, physical and social norms, the environment with its variable…
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…
Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection
Amirhossein Ansari, Masoud Dadgar, Ali Hamzeh +2
Today, many companies take advantage of viral marketing to promote their new products, and since there are several competing companies in many markets, Competitive Influence Maximi…
Parallel Total Variation Distance Estimation with Neural Networks for Merging Over-Clusterings
Christian Reiser, Jörg Schlötterer, Michael Granitzer
We consider the initial situation where a dataset has been over-partitioned into clusters and seek a domain independent way to merge those initial clusters. We identify the tot…
Policy Learning for Malaria Control
Van Bach Nguyen, Belaid Mohamed Karim, Bao Long Vu +2
Sequential decision making is a typical problem in reinforcement learning with plenty of algorithms to solve it. However, only a few of them can work effectively with a very small…
Structural Analysis of Sparse Neural Networks
Julian Stier, Michael Granitzer
Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages. We give motivation to study Artificial Neural Networks (ANNs) from a…
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs
Yvan Lucas, Pierre-Edouard Portier, Léa Laporte +4
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated ev…