Search Results for author: Thomas Seidl

Found 11 papers, 7 papers with code

Towards a Holistic View on Argument Quality Prediction

no code implementations19 May 2022 Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl

While there are works on the automated estimation of argument strength, their scope is narrow: they focus on isolated datasets and neglect the interactions with related argument mining tasks, such as argument identification, evidence detection, or emotional appeal.

Argument Mining

Adaptive Multi-Resolution Attention with Linear Complexity

no code implementations10 Aug 2021 Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp

Transformers have improved the state-of-the-art across numerous tasks in sequence modeling.

NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification

no code implementations5 Mar 2021 Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl

Malicious software (malware) poses an increasing threat to the security of communication systems as the number of interconnected mobile devices increases exponentially.

General Classification Malware Detection

Argument Mining Driven Analysis of Peer-Reviews

2 code implementations10 Dec 2020 Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work.

Argument Mining

Diversity Aware Relevance Learning for Argument Search

1 code implementation4 Nov 2020 Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman

In this work, we focus on the problem of retrieving relevant arguments for a query claim covering diverse aspects.

Argument Retrieval

Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels

1 code implementation23 Oct 2020 Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl

Furthermore, our experiments show that exponential moving average (EMA) of model parameters, which is a component of both algorithms, is not needed for our classification problem, as disabling it leaves the outcome unchanged.

Retinal OCT Disease Classification Semi-Supervised Image Classification +1

Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering

1 code implementation27 Sep 2020 Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl

Consequently, our model benefits from a constant number of parameters and a constant-size memory footprint, allowing it to scale to considerably larger datasets.

Metric Learning

PushNet: Efficient and Adaptive Neural Message Passing

1 code implementation4 Mar 2020 Julian Busch, Jiaxing Pi, Thomas Seidl

We find that our models outperform competitors on all datasets in terms of accuracy with statistical significance.

Node Classification Representation Learning

Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned

1 code implementation19 Nov 2019 Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl

In this work, we focus on the problem of entity alignment in Knowledge Graphs (KG) and we report on our experiences when applying a Graph Convolutional Network (GCN) based model for this task.

Entity Alignment Knowledge Graphs

TACAM: Topic And Context Aware Argument Mining

no code implementations26 May 2019 Michael Fromm, Evgeniy Faerman, Thomas Seidl

In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them.

Argument Mining Knowledge Graphs

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