M. Sc. Yujiang He

AI for Computationally Intelligent Systems (AI4CIS)

Telefon: +49 561 804-6184
Fax: +49 561 804-6022
E-Mail: Yujiang.he[at]uni-kassel[dot]de

Standort: Wilhelmshöher Allee 67
34121 Kassel

Raum: 1106

Publikationen

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

2022[ to top ]

Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts. He, Yujiang. In Organic Computing -- Doctoral Dissertation Colloquium 2021, S. Tomforde, C. Krupitzer (reds.), bll 125–140. kassel university press, 2022.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.
@incollection{he2022adaptive, abstract = {Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.}, author = {He, Yujiang}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2021}, editor = {Tomforde, Sven and Krupitzer, Christian}, keywords = {itegpub}, pages = {125–140}, publisher = {kassel university press}, title = {Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts}, year = 2022 }
%0 Book Section %1 he2022adaptive %A He, Yujiang %B Organic Computing -- Doctoral Dissertation Colloquium 2021 %D 2022 %E Tomforde, Sven %E Krupitzer, Christian %I kassel university press %P 125--140 %T Adaptive Explainable Continual Learning Framework for Regression Problems with Focus on Power Forecasts %X Compared with traditional deep learning techniques, continual learning enables deep neural networks to learn continually and adaptively. Deep neural networks have to learn unseen tasks and overcome forgetting the knowledge obtained from previously learned tasks as the amount of data keeps increasing in applications. This article proposes two continual learning application scenarios, i.e., the target-domain incremental scenario and the data-domain incremental scenario, to describe the potential challenges in this context. Based on our previous work regarding the CLeaR framework, which is short for continual learning for regression tasks, models will be enabled to extend themselves and to learn data successively. Research topics are related, but not limited, to developing continual deep learning algorithms, strategies for non-stationarity detection in data streams, explainable and visualizable artificial intelligence, etc. Moreover, the framework- and algorithm-related hyperparameters should be dynamically updated in applications. Forecasting experiments will be conducted based on power generation and consumption data collected from real-world applications. A series of comprehensive evaluation metrics and visualization tools are applied to access the experimental results. The proposed framework is expected to be generally applied to other constantly changing scenarios.

2021[ to top ]

Uncertainty and Utility Sampling with Pre-Clustering. Huang, Zhixin; He, Yujiang; Vogt, Stephan; Sick, Bernhard. In Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. 2021.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{huang2021uncertainty, author = {Huang, Zhixin and He, Yujiang and Vogt, Stephan and Sick, Bernhard}, booktitle = {Workshop on Interactive Adaptive Learning (IAL), ECML PKDD}, keywords = {itegpub}, note = {(accepted)}, title = {Uncertainty and Utility Sampling with Pre-Clustering}, year = 2021 }
%0 Conference Paper %1 huang2021uncertainty %A Huang, Zhixin %A He, Yujiang %A Vogt, Stephan %A Sick, Bernhard %B Workshop on Interactive Adaptive Learning (IAL), ECML PKDD %D 2021 %T Uncertainty and Utility Sampling with Pre-Clustering
Toward Application of Continuous Power Forecasts in a Regional Flexibility Market. He, Yujiang; Huang, Zhixin; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), bll 1–8. IEEE, 2021.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{he2021application, author = {He, Yujiang and Huang, Zhixin and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–8}, publisher = {IEEE}, title = {Toward Application of Continuous Power Forecasts in a Regional Flexibility Market}, year = 2021 }
%0 Conference Paper %1 he2021application %A He, Yujiang %A Huang, Zhixin %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2021 %I IEEE %P 1--8 %R 10.1109/ijcnn52387.2021.9533626 %T Toward Application of Continuous Power Forecasts in a Regional Flexibility Market

2020[ to top ]

Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks. He, Y.; Henze, J.; Sick, B. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{he2020forecasting, author = {{He}, Y. and {Henze}, J. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks}, year = 2020 }
%0 Conference Paper %1 he2020forecasting %A He, Y. %A Henze, J. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9207536 %T Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks
Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets. He, Yujiang; Henze, Janosch; Sick, Bernhard. In International Federation of Automatic Control (IFAC) World Congress, bll 12175–12182. Elsevier, 2020.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{he2020continuous, author = {He, Yujiang and Henze, Janosch and Sick, Bernhard}, booktitle = {{International Federation of Automatic Control (IFAC) World Congress}}, keywords = {itegpub}, pages = {12175–12182}, publisher = {Elsevier}, title = {Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets}, year = 2020 }
%0 Conference Paper %1 he2020continuous %A He, Yujiang %A Henze, Janosch %A Sick, Bernhard %B International Federation of Automatic Control (IFAC) World Congress %D 2020 %I Elsevier %P 12175--12182 %R 10.1016/j.ifacol.2020.12.1017 %T Continuous Learning of Deep Neural Networks to Improve Forecasts for Regional Energy Markets

2017[ to top ]

TPM Framework: a Comprehensive Kit for Exploring Applications with Textile Pressure Mapping Matrix. Zhou, Bo; Cheng, Jingyuan; Mawandia, Ankur; He, Yujiang; Huang, Zhixin; Sundholm, Mathias; Yildrim, Muhammet; Cruz, Heber; Lukowicz, Paul. In International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM). IARIA, 2017.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{zhou2017tpm, author = {Zhou, Bo and Cheng, Jingyuan and Mawandia, Ankur and He, Yujiang and Huang, Zhixin and Sundholm, Mathias and Yildrim, Muhammet and Cruz, Heber and Lukowicz, Paul}, booktitle = {International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM)}, keywords = {not_ies}, publisher = {IARIA}, title = {TPM Framework: a Comprehensive Kit for Exploring Applications with Textile Pressure Mapping Matrix}, year = 2017 }
%0 Conference Paper %1 zhou2017tpm %A Zhou, Bo %A Cheng, Jingyuan %A Mawandia, Ankur %A He, Yujiang %A Huang, Zhixin %A Sundholm, Mathias %A Yildrim, Muhammet %A Cruz, Heber %A Lukowicz, Paul %B International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) %D 2017 %I IARIA %T TPM Framework: a Comprehensive Kit for Exploring Applications with Textile Pressure Mapping Matrix