Evgeniy Faerman
Evgeniy Faerman
Verified email at dbs.ifi.lmu.de - Homepage
Title
Cited by
Cited by
Year
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
182018
Interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank
M Berrendorf, E Faerman, L Vermue, V Tresp
arXiv e-prints, arXiv: 2002.06914, 2020
112020
Knowledge graph entity alignment with graph convolutional networks: Lessons learned
M Berrendorf, E Faerman, V Melnychuk, V Tresp, T Seidl
Advances in Information Retrieval 12036, 3, 2020
102020
TACAM: Topic And Context Aware Argument Mining
M Fromm, E Faerman, T Seidl
2019 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 99-106, 2019
92019
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
82020
Argument Mining Driven Analysis of Peer-Reviews
M Fromm, E Faerman, M Berrendorf, S Bhargava, R Qi, Y Zhang, ...
arXiv preprint arXiv:2012.07743, 2020
72020
Active learning for entity alignment
M Berrendorf, E Faerman, V Tresp
European Conference on Information Retrieval, 48-62, 2021
52021
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
42019
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
42019
Semi-Supervised Learning on Graphs Based on Local Label Distributions
E Faerman, F Borutta, J Busch, M Schubert
arXiv preprint arXiv:1802.05563, 2018
32018
A Critical Assessment of State-of-the-Art in Entity Alignment
M Berrendorf, L Wacker, E Faerman
European Conference on Information Retrieval, 18-32, 2021
22021
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering
J Busch, E Faerman, M Schubert, T Seidl
arXiv preprint arXiv:2009.12875, 2020
22020
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
22020
Diversity Aware Relevance Learning for Argument Search
M Fromm, M Berrendorf, S Obermeier, T Seidl, E Faerman
European Conference on Information Retrieval, 264-271, 2021
12021
Spatial Interpolation with Message Passing Framework
E Faerman, M Rogalla, N Strauß, A Krüger, B Blümel, M Berrendorf, ...
2019 International Conference on Data Mining Workshops (ICDMW), 135-141, 2019
12019
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
12019
Active Learning for Argument Strength Estimation
N Kees, M Fromm, E Faerman, T Seidl
arXiv preprint arXiv:2109.11319, 2021
2021
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, ...
arXiv preprint arXiv:2105.15108, 2021
2021
Representation learning on relational data
E Faerman
lmu, 2021
2021
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
2020
The system can't perform the operation now. Try again later.
Articles 1–20