Profile Page Prof. Dr. Damian Borth

Profile Picture
Name Damian Borth
Title Prof. Dr.
Function
Institute/School ICS - Institute of Computer Science
Address School of Computer Science (SCS) - Institute of Computer Science (ICS-HSG)
Büro 61-510
Rosenbergstrasse 30
9000 St. Gallen
Schweiz
Email address damian.borth@unisg.ch
Phone +41 71 224 26 27
Homepage https://ics.unisg.ch/chair-aiml-borth/
Twitter https://twitter.com/damianborth
Google Scholar https://scholar.google.de/citations?user=J-8Z038AAAAJ
Main Focuses Maschinelles Lernen, Deep Learning, Künstliche Intelligenz, Satelliten Bildanalyse
Education
Teaching Activities
Affiliations
Awards
Additional Information

Latest Additions (all)

  1. Item Scheibenreif, Linus Mathias; Hanna, Joëlle; Mommert, Michael & Borth, Damian: Self-supervised Vision Transformers for Land-cover Segmentation and Classification. 2022. [img]
  2. Item Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian (2022) Toward Global Estimation of Ground-Level NO2 Pollution With Deep Learning and Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing, 60 (4705914). 1-14. ISSN 0196-2892 [img]
  3. Item Hemati, Hamed; Schreyer, Marco & Borth, Damian: Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data. 2022. - AAAI 2022 Workshop on AI in Financial Services: Adaptiveness, Resilience & Governance. - Virtual. [img]
  4. Item Schreyer, Marco; Gierbl, Anita Stefanie; Ruud, Flemming & Borth, Damian (2022) Stichprobenauswahl durch die Anwendung von Künstlicher Intelligenz - Lernen repräsentativer Stichproben aus Journalbuchungen in der Prüfungspraxis. Expert Focus, (02). 10-18. [img]
  5. Item Schreyer, Marco; Baumgartner, Marcel; Ruud, Flemming & Borth, Damian (2022) Artificial Intelligence in Internal Audit as a Contribution to Effective Governance - Deep-learning enabled Detection of Anomalies in Financial Accounting Data. Expert Focus, Special: Internal Audit (01). 39-44. [img]
  6. Item Schreyer, Marco; Baumgartner, Marcel; Ruud, Flemming & Borth, Damian (2022) Künstliche Intelligenz im Internal Audit als Beitrag zur Effektiven Governance - Deep-Learning basierte Detektion von Buchungsanomalien in der Revisionspraxis. Expert Focus, Special: Interne Revision (01). 39-44. [img]
  7. Item Scheibenreif, Linus Mathias; Mommert, Michael & Borth, Damian: Contrastive Self-Supervised Data Fusion for Satellite Imagery. 2022. - ISPRS Congress. - Nice. [img]
  8. Item Hanna, Joëlle; Mommert, Michael; Scheibenreif, Linus Mathias & Borth, Damian: Multitask Learning for Estimating Power Plant Greenhouse Gas Emissions from Satellite Imagery. 2021. [img]
  9. Item Schürholt, Konstantin; Kostadinov, Dimche & Borth, Damian: Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction. 2021. - Neural Information Processing Systems (NeurIPS). [img]
  10. Item Schreyer, Marco; Sattarov, Timur & Borth, Damian: Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks. 2021. - ACM International Conference on Artificial Intelligence in Finance (ICAIF). - London, United Kingdom. [img]
Feedback?