20 Publikationen

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  • [20]
    2023 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2981289
    Hinder F, Vaquet V, Brinkrolf J, Hammer B. Model-based explanations of concept drift. Neurocomputing. 2023: 126640.
    PUB | DOI | Download (ext.) | WoS
     
  • [19]
    2023 | Konferenzbeitrag | Angenommen | PUB-ID: 2982899 OA
    Vaquet V, Brinkrolf J, Hammer B. Robust Feature Selection and Robust Training to Cope with Hyperspectral Sensor Shifts.
    PUB | PDF
     
  • [18]
    2023 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2982167
    Hinder F, Vaquet V, Brinkrolf J, Hammer B. On the Hardness and Necessity of Supervised Concept Drift Detection. In: De Marsico M, Sanniti di Baja G, Fred A, eds. Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods ICPRAM. Vol. 1. Setúbal: SCITEPRESS - Science and Technology Publications; 2023: 164-175.
    PUB | DOI
     
  • [17]
    2023 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2977934
    Hinder F, Vaquet V, Brinkrolf J, Hammer B. On the Change of Decision Boundary and Loss in Learning with Concept Drift. In: Crémilleux B, Hess S, Nijssen S, eds. Advances in Intelligent Data Analysis XXI. 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12–14, 2023, Proceedings. Lecture Notes in Computer Science. Vol 13876. Cham: Springer ; 2023: 182-194.
    PUB | DOI
     
  • [16]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962746 OA
    Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B. Contrasting Explanations for Understanding and Regularizing Model Adaptations. Neural Processing Letters. 2022;55:5273–5297.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [15]
    2022 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2984050
    Hinder F, Vaquet V, Hammer B. Suitability of Different Metric Choices for Concept Drift Detection. In: Bouadi T, Fromont E, Hüllermeier E, eds. Advances in Intelligent Data Analysis XX. 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20–22, 2022, Proceedings. Lecture Notes in Computer Science. Cham: Springer International Publishing; 2022: 157-170.
    PUB | DOI
     
  • [14]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2966088
    Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B. Localization of Concept Drift: Identifying the Drifting Datapoints. In: 2022 International Joint Conference on Neural Networks (IJCNN). IEEE; 2022: 1-9.
    PUB | DOI | Download (ext.)
     
  • [13]
    2022 | Konferenzbeitrag | Angenommen | PUB-ID: 2964534
    Vaquet V, Hinder F, Brinkrolf J, Menz P, Seiffert U, Hammer B. Federated learning vector quantization for dealing with drift between nodes. Presented at the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2022, Bruges.
    PUB
     
  • [12]
    2022 | Zeitschriftenaufsatz | E-Veröff. vor dem Druck | PUB-ID: 2962928
    Vaquet V, Menz P, Seiffert U, Hammer B. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. Neurocomputing. 2022.
    PUB | DOI | WoS
     
  • [11]
    2022 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2962861
    Hinder F, Vaquet V, Brinkrolf J, Artelt A, Hammer B. Localization of Concept Drift: Identifying the Drifting Datapoints.
    PUB
     
  • [10]
    2022 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962650 OA
    Vaquet V, Artelt A, Brinkrolf J, Hammer B. Taking care of our drinking water: Dealing with Sensor Faults in Water Distribution Networks. Presented at the 31st International Conference on Artificial Neural Networks, Bristol.
    PUB | PDF
     
  • [9]
    2021 | Konferenzbeitrag | PUB-ID: 2959428
    Hinder F, Vaquet V, Brinkrolf J, Hammer B. Fast Non-Parametric Conditional Density Estimation using Moment Trees. IEEE Computational Intelligence Magazine. 2021.
    PUB
     
  • [8]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960687
    Vaquet V, Hinder F, Vaquet J, Brinkrolf J, Hammer B. Online Learning on Non-Stationary Data Streams for Image Recognition using Deep Embeddings. IEEE Symposium Series on Computational Intelligence. 2021:1-7.
    PUB | DOI
     
  • [7]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960754
    Hinder F, Brinkrolf J, Vaquet V, Hammer B. A Shape-Based Method for Concept Drift Detection and Signal Denoising. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE; 2021: 01-08.
    PUB | DOI
     
  • [6]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960755
    Hinder F, Vaquet V, Brinkrolf J, Hammer B. Fast Non-Parametric Conditional Density Estimation using Moment Trees. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings. Piscataway, NJ: IEEE; 2021: 1-7.
    PUB | DOI
     
  • [5]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2960685
    Vaquet V, Menz P, Seiffert U, Hammer B. Investigating Intensity and Transversal Drift in Hyperspectral Imaging Data. In: Verleysen M, ed. ESANN 2021 proceedings. Louvain-la-Neuve (Belgium): Ciaco - i6doc.com; 2021: 47-52.
    PUB | DOI
     
  • [4]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2957373
    Artelt A, Hinder F, Vaquet V, Feldhans R, Hammer B. Contrastive Explanations for Explaining Model Adaptations. In: Rojas I, Joya G, Catala A, eds. Advances in Computational Intelligence. 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I. Lecture Notes in Computer Science. Cham: Springer ; 2021: 101-112.
    PUB | DOI
     
  • [3]
    2021 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2962747
    Artelt A, Vaquet V, Velioglu R, et al. Evaluating Robustness of Counterfactual Explanations. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI). Piscataway, NJ: IEEE; 2021: 01-09.
    PUB | DOI
     
  • [2]
    2020 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2958328
    Vaquet V, Hammer B. Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data. In: Farkaš I, Masulli P, Wermter S, eds. Artificial Neural Networks and Machine Learning – ICANN 2020. 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II. Lecture Notes in Computer Science. Vol 12397. Cham: Springer; 2020: 850-862.
    PUB | DOI
     
  • [1]
    2019 | Kurzbeitrag Konferenz / Poster | PUB-ID: 2935044 OA
    Artelt A, Jakob J, Vaquet V. Continuous online user authentication based on keystroke dynamics. Presented at the Interdisciplinary College (IK), Günne/Möhnesee, Germany.
    PUB | Dateien verfügbar
     

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