Forschungsbereich Machine Learning

Organization Name (de) Name der Organisation (de)
E194-06 - Forschungsbereich Machine Learning
 
Code Kennzahl
E194-06
 
Type of Organization Organisationstyp
Research Division
Parent OrgUnit Übergeordnete Organisation
 
Active Aktiv
 


Results 21-29 of 29 (Search time: 0.002 seconds).

PreviewAuthor(s)TitleTypeIssue Date
21Schedl, Markus ; Brandl, Stefan ; Lesota, Oleg ; Parada-Cabaleiro, Emilia ; Penz, David ; Rekabsaz, Navid LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness AnalysisKonferenzbeitrag Inproceedings 2022
22Thiessen, Maximilian ; Gärtner, Thomas Online learning of convex sets on graphsInproceedings Konferenzbeitrag 2022
23Indri, Patrick ; Bartoli, Alberto ; Medvet, Eric ; Nenzi, Laura One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical SystemsKonferenzbeitrag Inproceedings 2022
24Thiessen-2021-Active Learning Convex Halfspaces on Graphs-am.pdf.jpgThiessen, Maximilian ; Gärtner, Thomas Active Learning Convex Halfspaces on GraphsInproceedings Konferenzbeitrag 24-Jul-2021
25Krauck, Alexander ; Penz, David ; Schedl, Markus Team JKU-AIWarriors in the ACM Recommender Systems Challenge 2021: Lightweight XGBoost Recommendation Approach Leveraging User FeaturesKonferenzbeitrag Inproceedings 2021
26Thiessen, Maximilian ; Gärtner, Thomas Active Learning of Convex Halfspaces on GraphsKonferenzbeitrag Inproceedings 2021
27Thiessen-2021-Active Learning of Convex Halfspaces on Graphs-am.pdf.jpgThiessen, Maximilian ; Gärtner, Thomas Active Learning of Convex Halfspaces on GraphsInproceedings Konferenzbeitrag 2021
28Schmied-2020-Efficient Reinforcement Learning via Self-supervised learning...-am.pdf.jpgSchmied, Thomas ; Thiessen, Maximilian Efficient Reinforcement Learning via Self-supervised learning and Model-based methodsInproceedings Konferenzbeitrag 12-Dec-2020
29Thiessen-2020-Active Learning on Graphs with Geodesically Convex Classes-ao.pdf.jpgThiessen, Maximilian ; Gärtner, Thomas Active Learning on Graphs with Geodesically Convex ClassesInproceedings Konferenzbeitrag 24-Aug-2020