Konstantin Kobs, M.Sc.
Chair of Data Science (Informatik X)
University of Würzburg
Phone: (+49 931) 31 - 84707
Office: Room B100 (Computer Science Building M2)
Projects and Research Interests
I am working on Machine Learning and Neural Network models to process text, images, audio, and sensor data. The idea is to utilize additional information such as data attributes to learn representations that can be used in tasks, such as infromation retrieval or classification.
In the BigData@Geo project, we work with climate data, so time series and geospatial data. Our goal is to help the geography department to improve climate modeling using Machine Learning.
- 2015–2017: M.Sc. Computer Science at the University of Hamburg
- 2012–2015: B.Sc. Computer Science at the University of Hamburg
Do Different Deep Metric Learning Losses Lead to Similar Learned Features?, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10644–10654.(2021)
Density-based weighting for imbalanced regression, Machine Learning, available: https://doi.org/10.1007/s10994-021-06023-5.(2021)
Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment, in Proceedings of the IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 816–825.(2021)
Semi-Supervised Learning for Grain Size Distribution Interpolation, in Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI, Springer International Publishing, 34–44.(2021)
NICER — Aesthetic Image Enhancement with Humans in the Loop, in ACHI 2020: The Thirteenth International Conference on Advances in Computer-Human Interactions, 357–362.(2020)
Towards Predicting the Subscription Status of Twitch.tv Users, Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch, available: http://ceur-ws.org/Vol-2661/paper1.pdf.(2020)
Where to Submit? Helping Researchers to Choose the Right Venue, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, Online: Association for Computational Linguistics, 878–883, available: https://www.aclweb.org/anthology/2020.findings-emnlp.78.(2020)
Improving Sentiment Analysis with Biofeedback Data, in Proceedings of the Workshop on PeOple in LaNguage, VIsiOn and the MiNd (ONION), available: https://downloads.hci.informatik.uni-wuerzburg.de/2018-ieeevr-lugrin-vr-teacher-training/2020-onion-sentiment-eeg-preprint.pdf.(2020)
OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning, Atmospheric Environment, 233, 117535, available: https://doi.org/https://doi.org/10.1016/j.atmosenv.2020.117535.(2020)
MapLUR: Exploring a new Paradigm for Estimating Air Pollution using Deep Learning on Map Images, available: https://arxiv.org/abs/2002.07493.(2020)
Anomaly Detection in Beehives using Deep Recurrent Autoencoders, in Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), SCITEPRESS – Science and Technology Publications, Lda., 142–149.(2020)
- [ BibTeX ]
SimLoss: Class Similarities in Cross Entropy, available: http://arxiv.org/abs/2003.03182.(2020)
Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels, ACM Transactions on Social Computing, available: https://doi.org/10.1145/3365523.(2020)