Pu­bli­ka­tio­nen

[ 2022 ] [ 2021 ] [ 2020 ] [ 2017 ]

2022 [ nach oben ]

  • 1.
    He, Y., Huang, Z., Sick, B.: Design of Explainability Module with Experts in the Loop for Visualization and Dynamic Adjustment of Continual Learning. Workshop on Interactive Machine Learning Workshop (IMLW), AAAI. bll. 1–6 (2022).
     
  • 2.
    He, Y.: Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts. In: Tomforde, S. en Krupitzer, C. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2021. bll. 125–140. kassel university press (2022).
     

2021 [ nach oben ]

  • 1.
    Huang, Z., He, Y., Vogt, S., Sick, B.: Uncertainty and Utility Sampling with Pre-Clustering. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD (2021).
     
  • 2.
    He, Y., Huang, Z., Sick, B.: Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (2021).
     
  • 3.
    He, Y., Sick, B.: CLeaR: An adaptive continual learning framework for regression tasks. AI Perspectives. 3, 2 (2020).
     

2020 [ nach oben ]

  • 1.
    He, Y., Henze, J., Sick, B.: Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. International Joint Conference on Neural Networks (IJCNN). IEEE (2020).
     
  • 2.
    He, Y., Henze, J., Sick, B.: Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. International Federation of Automatic Control (IFAC) World Congress. bll. 12175–12182. Elsevier (2020).
     

2017 [ nach oben ]

  • 1.
    Zhou, B., Cheng, J., Mawandia, A., He, Y., Huang, Z., Sundholm, M., Yildrim, M., Cruz, H., Lukowicz, P.: TPM Framework: a Comprehensive Kit for Exploring Applications with Textile Pressure Mapping Matrix. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM). IARIA (2017).