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Evgeniy Faerman
Evgeniy Faerman
Verified email at dbs.ifi.lmu.de - Homepage
Title
Cited by
Cited by
Year
TACAM: topic and context aware argument mining
M Fromm, E Faerman, T Seidl
IEEE/WIC/ACM International Conference on Web Intelligence, 99-106, 2019
282019
Lasagne: Locality and structure aware graph node embedding
E Faerman, F Borutta, K Fountoulakis, MW Mahoney
2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 246-253, 2018
272018
Argument mining driven analysis of peer-reviews
M Fromm, E Faerman, M Berrendorf, S Bhargava, R Qi, Y Zhang, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 4758-4766, 2021
252021
Active learning for entity alignment
M Berrendorf, E Faerman, V Tresp
Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021
252021
Unsupervised Anomaly Detection for X-Ray Images
D Davletshina, V Melnychuk, V Tran, H Singla, M Berrendorf, E Faerman, ...
arXiv preprint arXiv:2001.10883, 2020
242020
Knowledge graph entity alignment with graph convolutional networks: lessons learned
M Berrendorf, E Faerman, V Melnychuk, V Tresp, T Seidl
Advances in Information Retrieval: 42nd European Conference on IR Research …, 2020
242020
On the Ambiguity of Rank-Based Evaluation of Entity Alignment or Link Prediction Methods
M Berrendorf, E Faerman, L Vermue, V Tresp
arXiv preprint arXiv:2002.06914, 2020
162020
Interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank
M Berrendorf, E Faerman, L Vermue, V Tresp
arXiv preprint arXiv:2002.06914, 2020
152020
Graph Alignment Networks with Node Matching Scores
E Faerman, O Voggenreiter, F Borutta, T Emrich, M Berrendorf, ...
Proceedings of Advances in Neural Information Processing Systems (NIPS), 2019
102019
A critical assessment of state-of-the-art in entity alignment
M Berrendorf, L Wacker, E Faerman
Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021
82021
Interpretable and Fair Comparison of Link Prediction or Entity Alignment Methods
M Berrendorf, E Faerman, L Vermue, V Tresp
2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and …, 2020
72020
Towards a Holistic View on Argument Quality Prediction
M Fromm, M Berrendorf, J Reiml, I Mayerhofer, S Bhargava, E Faerman, ...
arXiv preprint arXiv:2205.09803, 2022
62022
Prediction of soft proton intensities in the near-Earth space using machine learning
EA Kronberg, T Hannan, J Huthmacher, M Münzer, F Peste, Z Zhou, ...
The Astrophysical Journal 921 (1), 76, 2021
62021
Structural Graph Representations based on Multiscale Local Network Topologies
F Borutta, J Busch, E Faerman, A Klink, M Schubert
IEEE/WIC/ACM International Conference on Web Intelligence, 91-98, 2019
62019
Semi-Supervised Learning on Graphs Based on Local Label Distributions
E Faerman, F Borutta, J Busch, M Schubert
arXiv preprint arXiv:1802.05563, 2018
62018
Active Learning for Argument Strength Estimation
N Kees, M Fromm, E Faerman, T Seidl
arXiv preprint arXiv:2109.11319, 2021
52021
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
J Busch, E Faerman, M Schubert, T Seidl
arXiv preprint arXiv:2009.12875, 2020
52020
Diversity Aware Relevance Learning for Argument Search
M Fromm, M Berrendorf, S Obermeier, T Seidl, E Faerman
Advances in Information Retrieval: 43rd European Conference on IR Research …, 2021
42021
XD-STOD: Cross-Domain Superresolution for Tiny Object Detection
M Fromm, M Berrendorf, E Faerman, Y Chen, B Schüss, M Schubert
2019 International Conference on Data Mining Workshops (ICDMW), 142-148, 2019
32019
Cross-Domain Argument Quality Estimation
M Fromm, M Berrendorf, E Faerman, T Seidl
Findings of the Association for Computational Linguistics: ACL 2023, 13435-13448, 2023
22023
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