| Tensor-train recurrent neural networks for video classification Y Yang, D Krompass, V Tresp International Conference on Machine Learning, 3891-3900, 2017 | 282 | 2017 |
| Predicting clinical events by combining static and dynamic information using recurrent neural networks C Esteban, O Staeck, S Baier, Y Yang, V Tresp 2016 IEEE International Conference on Healthcare Informatics (ICHI), 93-101, 2016 | 201 | 2016 |
| Understanding individual decisions of cnns via contrastive backpropagation J Gu, Y Yang, V Tresp Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 122 | 2019 |
| Explaining therapy predictions with layer-wise relevance propagation in neural networks Y Yang, V Tresp, M Wunderle, PA Fasching 2018 IEEE International Conference on Healthcare Informatics (ICHI), 152-162, 2018 | 110 | 2018 |
| Predicting the co-evolution of event and knowledge graphs C Esteban, V Tresp, Y Yang, S Baier, D Krompaß 2016 19th International Conference on Information Fusion (FUSION), 98-105, 2016 | 55 | 2016 |
| Predictive modeling of therapy decisions in metastatic breast cancer with recurrent neural network encoder and multinomial hierarchical regression decoder Y Yang, PA Fasching, V Tresp 2017 IEEE international conference on healthcare informatics (ICHI), 46-55, 2017 | 37 | 2017 |
| Learning with memory embeddings V Tresp, C Esteban, Y Yang, S Baier, D Krompaß arXiv preprint arXiv:1511.07972, 2015 | 33 | 2015 |
| Embedding learning for declarative memories V Tresp, Y Ma, S Baier, Y Yang The Semantic Web: 14th International Conference, ESWC 2017, Portorož …, 2017 | 25 | 2017 |
| Modeling progression free survival in breast cancer with tensorized recurrent neural networks and accelerated failure time models Y Yang, PA Fasching, V Tresp Machine Learning for Healthcare Conference, 164-176, 2017 | 19 | 2017 |
| Predictive clinical decision support system with RNN encoding and tensor decoding Y Yang, PA Fasching, M Wallwiener, TN Fehm, SY Brucker, V Tresp arXiv preprint arXiv:1612.00611, 2016 | 12 | 2016 |
| Categorical ehr imputation with generative adversarial nets Y Yang, Z Wu, V Tresp, PA Fasching 2019 IEEE International Conference on Healthcare Informatics (ICHI), 1-10, 2019 | 10 | 2019 |
| Uncertainty-aware time-to-event prediction using deep kernel accelerated failure time models Z Wu, Y Yang, PA Fashing, V Tresp Machine Learning for Healthcare Conference, 54-79, 2021 | 8 | 2021 |
| Quantifying predictive uncertainty in medical image analysis with deep kernel learning Z Wu, Y Yang, J Gu, V Tresp 2021 IEEE 9th international conference on healthcare informatics (ICHI), 63-72, 2021 | 7 | 2021 |
| Healthcare Informatics (ICHI) Y Yang, PA Fasching, V Tresp 2017 IEEE International Conference on, 46-55, 2017 | 7 | 2017 |
| Multi-output gaussian processes for uncertainty-aware recommender systems Y Yang, F Buettner Uncertainty in Artificial Intelligence, 1505-1514, 2021 | 6 | 2021 |
| Embedding mapping approaches for tensor factorization and knowledge graph modelling Y Yang, C Esteban, V Tresp The Semantic Web. Latest Advances and New Domains: 13th International …, 2016 | 5 | 2016 |
| Learning individualized treatment rules with estimated translated inverse propensity score Z Wu, Y Yang, Y Ma, Y Liu, R Zhao, M Moor, V Tresp 2020 IEEE International Conference on Healthcare Informatics (ICHI), 1-11, 2020 | 2 | 2020 |
| Modeling clinical decisions with multinomial hierarchical classification Y Yang, PA Fasching, V Tresp Proc Machine Learning Res (Sydney, Australia), 2017 | 2 | 2017 |
| Learning representations with multi-output Gaussian processes for uncertainty-aware collaborative filtering Y Yang, F Buettner Proceedings of Extended Semantic Web Conference, 2021 | 1 | 2021 |
| Determining influence of attributes in recurrent neural net-works trained on therapy prediction V Tresp, Y Yang US Patent App. 16/398,615, 2019 | 1 | 2019 |
