Search Results for author: Michael Granitzer

Found 34 papers, 13 papers with code

GRhOOT: Ontology of Rhetorical Figures in German

1 code implementation LREC 2022 Ramona Kühn, Jelena Mitrović, Michael Granitzer

GRhOOT, the German RhetOrical OnTology, is a domain ontology of 110 rhetorical figures in the German language.

Argument Mining Machine Translation +1

Towards Measuring Representational Similarity of Large Language Models

1 code implementation5 Dec 2023 Max Klabunde, Mehdi Ben Amor, Michael Granitzer, Florian Lemmerich

Understanding the similarity of the numerous released large language models (LLMs) has many uses, e. g., simplifying model selection, detecting illegal model reuse, and advancing our understanding of what makes LLMs perform well.

Model Selection

Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings

no code implementations3 Aug 2023 Veronika Hackl, Alexandra Elena Müller, Michael Granitzer, Maximilian Sailer

Statistical analysis was conducted in order to learn more about the interrater reliability, consistency of the ratings across iterations and the correlation between ratings in terms of content and style.

Language Modelling Large Language Model

GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators

1 code implementation13 Jul 2023 Ousmane Touat, Julian Stier, Pierre-Edouard Portier, Michael Granitzer

We use these metrics to compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.

Drug Discovery Graph Embedding +2

Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping

no code implementations12 May 2023 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.

Image Segmentation Knowledge Distillation +1

Technical Report on Token Position Bias in Transformers

no code implementations26 Apr 2023 Mehdi Ben Amor, Michael Granitzer, Jelena Mitrović

In this paper, we investigate an additional specific issue for language models, namely the position bias of positive examples in token classification tasks.

named-entity-recognition Named Entity Recognition +7

German BERT Model for Legal Named Entity Recognition

no code implementations7 Mar 2023 Harshil Darji, Jelena Mitrović, Michael Granitzer

Even though there is much research done on NER using BERT and other popular language models, the same is not explored in detail when it comes to Legal NLP or Legal Tech.

Language Modelling named-entity-recognition +4

SmartKex: Machine Learning Assisted SSH Keys Extraction From The Heap Dump

1 code implementation12 Sep 2022 Christofer Fellicious, Stewart Sentanoe, Michael Granitzer, Hans P. Reiser

A commonly used method in digital forensics is to extract data from the main memory of a digital device.

The Importance of Future Information in Credit Card Fraud Detection

no code implementations11 Apr 2022 Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer, Wissam Siblini

We believe that future works on this new paradigm will have a significant impact on the detection of compromised cards.

Fraud Detection

deepstruct -- linking deep learning and graph theory

no code implementations12 Nov 2021 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 neural network models.

Neural Architecture Search

Experiments on Properties of Hidden Structures of Sparse Neural Networks

1 code implementation27 Jul 2021 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.

Neural Architecture Search

nlpUP at SemEval-2020 Task 12 : A Blazing Fast System for Offensive Language Detection

no code implementations SEMEVAL 2020 Ehab Hamdy, Jelena Mitrovi{\'c}, Michael Granitzer

In this paper, we introduce our submission for the SemEval Task 12, sub-tasks A and B for offensive language identification and categorization in English tweets.

Language Identification

ADSAGE: Anomaly Detection in Sequences of Attributed Graph Edges applied to insider threat detection at fine-grained level

no code implementations14 Jul 2020 Mathieu Garchery, Michael Granitzer

We evaluate ADSAGE on authentication, email traffic and web browsing logs from the CERT insider threat datasets, as well as on real-world authentication events.

Anomaly Detection Feature Engineering

DeepGG: a Deep Graph Generator

1 code implementation7 Jun 2020 Julian Stier, Michael Granitzer

Learning distributions of graphs can be used for automatic drug discovery, molecular design, complex network analysis, and much more.

Drug Discovery Graph Embedding

Language Proficiency Scoring

no code implementations LREC 2020 Cristina Arhiliuc, Jelena Mitrovi{\'c}, Michael Granitzer

The Common European Framework of Reference (CEFR) provides generic guidelines for the evaluation of language proficiency.

Investigating Extensions to Random Walk Based Graph Embedding

no code implementations17 Feb 2020 Joerg Schloetterer, Martin Wehking, Fatemeh Salehi Rizi, Michael Granitzer

Graph embedding has recently gained momentum in the research community, in particular after the introduction of random walk and neural network based approaches.

Graph Embedding Link Prediction +1

Predicting event attendance exploring social influence

no code implementations16 Feb 2020 Fatemeh Salehi Rizi, Michael Granitzer

In this paper, we propose to model the social influence of friends on event attendance.

Graph Embedding

Global and Local Feature Learning for Ego-Network Analysis

no code implementations16 Feb 2020 Fatemeh Salehi Rizi, Michael Granitzer, Konstantin Ziegler

This social network can be efficiently analyzed after learning representations of the ego and its alters in a low-dimensional, real vector space.

Language Modelling Learning Network Representations

Shortest path distance approximation using deep learning techniques

1 code implementation12 Feb 2020 Fatemeh Salehi Rizi, Joerg Schloetterer, Michael Granitzer

Computing shortest path distances between nodes lies at the heart of many graph algorithms and applications.

Competitive Influence Maximization: Integrating Budget Allocation and Seed Selection

1 code implementation27 Dec 2019 Amirhossein Ansari, Masoud Dadgar, Ali Hamzeh, Jörg Schlötterer, Michael Granitzer

In this work, we integrate these two lines of research and propose a new scenario where competition happens in two phases.

Social and Information Networks Computer Science and Game Theory

Parallel Total Variation Distance Estimation with Neural Networks for Merging Over-Clusterings

no code implementations9 Dec 2019 Christian Reiser, Jörg Schlötterer, Michael Granitzer

We consider the initial situation where a dataset has been over-partitioned into $k$ clusters and seek a domain independent way to merge those initial clusters.

Policy Learning for Malaria Control

2 code implementations20 Oct 2019 Van Bach Nguyen, Belaid Mohamed Karim, Bao Long Vu, Jörg Schlötterer, Michael Granitzer

In this report, we introduce the progress to learn the policy for Malaria Control as a Reinforcement Learning problem in the KDD Cup Challenge 2019 and propose diverse solutions to deal with the limited observations problem.

Bayesian Optimization Decision Making +3

Structural Analysis of Sparse Neural Networks

no code implementations16 Oct 2019 Julian Stier, Michael Granitzer

Sparse Neural Networks regained attention due to their potential for mathematical and computational advantages.

Neural Architecture Search

Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

1 code implementation3 Sep 2019 Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Liyun He-Guelton, Olivier Caelen, Michael Granitzer, Sylvie Calabretto

Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection.

Automated Feature Engineering Feature Engineering +1

nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection

no code implementations SEMEVAL 2019 Jelena Mitrovi{\'c}, Bastian Birkeneder, Michael Granitzer

In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets.

Language Modelling

Multiple perspectives HMM-based feature engineering for credit card fraud detection

1 code implementation15 May 2019 Yvan Lucas, Pierre-Edouard Portier, Léa Laporte, Olivier Caelen, Liyun He-Guelton, Sylvie Calabretto, Michael Granitzer

In this article, we model a sequence of credit card transactions from three different perspectives, namely (i) does the sequence contain a Fraud?

Feature Engineering Fraud Detection

Analysing Neural Network Topologies: a Game Theoretic Approach

no code implementations17 Apr 2019 Julian Stier, Gabriele Gianini, Michael Granitzer, Konstantin Ziegler

In previous work, heuristics based on using the weight distribution of a neuron as contribution measure have shown some success, but do not provide a proper theoretical understanding.

Network Pruning

Cannot find the paper you are looking for? You can Submit a new open access paper.