5 Publikationen
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2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993740Localizing of Anomalies in Critical Infrastructure using Model-Based Drift ExplanationsPUB | DOI | WoS
Vaquet, Valerie, Localizing of Anomalies in Critical Infrastructure using Model-Based Drift Explanations. 2024 International Joint Conference on Neural Networks (IJCNN) (). , 2024 -
2024 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2993739Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution NetworksPUB | DOI | WoS
Vaquet, Valerie, Challenges, Methods, Data–A Survey of Machine Learning in Water Distribution Networks. Artificial Neural Networks and Machine Learning – ICANN 2024. 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, Part IX (). Cham, 2024 -
2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2993744Self-Supervised Learning from Incrementally Drifting Data StreamsPUB | DOI
Vaquet, Valerie, Self-Supervised Learning from Incrementally Drifting Data Streams. ESANN 2024 proceesdings (). Louvain-la-Neuve (Belgium), 2024 -
2024 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2992337
A Water Futures Approach on Water Demand Forecasting with Online Ensemble LearningPUB | PDF | DOI
Zanutto, Dennis, A Water Futures Approach on Water Demand Forecasting with Online Ensemble Learning. The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024) (). Basel Switzerland, 2024 -
2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687Online Learning on Non-Stationary Data Streams for Image Recognition using Deep EmbeddingsPUB | DOI
Vaquet, Valerie, Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. (). , 2021