no code implementations • 19 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.
1 code implementation • EMNLP (insights) 2021 • Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl
High-quality arguments are an essential part of decision-making.
1 code implementation • 11 May 2021 • Elena A. Kronberg, Tanveer Hannan, Jens Huthmacher, Marcus Münzer, Florian Peste, Ziyang Zhou, Max Berrendorf, Evgeniy Faerman, Fabio Gastaldello, Simona Ghizzardi, Philippe Escoubet, Stein Haaland, Artem Smirnov, Nithin Sivadas, Robert C. Allen, Andrea Tiengo, Raluca Ilie
The average correlation between the observed and predicted data is about 56%, which is reasonable in light of the complex dynamics of fast-moving energetic protons in the magnetosphere.
2 code implementations • 10 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.
1 code implementation • 4 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.
1 code implementation • 30 Oct 2020 • Max Berrendorf, Ludwig Wacker, Evgeniy Faerman
Therefore, we first carefully examine the benchmarking process and identify several shortcomings, which make the results reported in the original works not always comparable.
Ranked #11 on Entity Alignment on dbp15k fr-en
1 code implementation • 23 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.
Ranked #3 on Retinal OCT Disease Classification on OCT2017
Retinal OCT Disease Classification Semi-Supervised Image Classification +1
1 code implementation • 27 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.
1 code implementation • 17 Feb 2020 • Max Berrendorf, Evgeniy Faerman, Laurent Vermue, Volker Tresp
In this work, we take a closer look at the evaluation of two families of methods for enriching information from knowledge graphs: Link Prediction and Entity Alignment.
1 code implementation • 29 Jan 2020 • Diana Davletshina, Valentyn Melnychuk, Viet Tran, Hitansh Singla, Max Berrendorf, Evgeniy Faerman, Michael Fromm, Matthias Schubert
Therefore, we adopt state-of-the-art approaches for unsupervised learning to detect anomalies and show how the outputs of these methods can be explained.
1 code implementation • 24 Jan 2020 • Max Berrendorf, Evgeniy Faerman, Volker Tresp
In this work, we propose a novel framework for the labeling of entity alignments in knowledge graph datasets.
1 code implementation • 19 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.
Ranked #33 on Entity Alignment on DBP15k zh-en
no code implementations • 26 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.
no code implementations • 15 Feb 2018 • Evgeniy Faerman, Felix Borutta, Julian Busch, Matthias Schubert
Precisely, we propose a new node embedding which is based on the class labels in the local neighborhood of a node.
no code implementations • 17 Oct 2017 • Evgeniy Faerman, Felix Borutta, Kimon Fountoulakis, Michael W. Mahoney
For larger graphs with flat NCPs that are strongly expander-like, existing methods lead to random walks that expand rapidly, touching many dissimilar nodes, thereby leading to lower-quality vector representations that are less useful for downstream tasks.