Property:References

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Pages using the property "References"

Showing 25 pages using this property.

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Exploring, modelling and optimization of home care regions +Madigan, E. A., & Curet, O. L. (2006). A data mining approach in home healthcare: outcomes and service use. BMC health services research, 6(1), 1. Cheng, B. W., Chang, C. L., & Liu, I. S. (2005). Enhancing care services quality of nursing homes using data mining. Total Quality Management & Business Excellence, 16(5), 575-596. Hirdes, J. P., Poss, J. W., & Curtin-Telegdi, N. (2008). The Method for Assigning Priority Levels (MAPLe): a new decision-support system for allocating home care resources. BMC medicine, 6(1), 1. Harrington, C., Zimmerman, D., Karon, S. L., Robinson, J., & Beutel, P. (2000). Nursing home staffing and its relationship to deficiencies. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 55(5), S278-S287.

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Fair Conformal Prediction +https://ojs.aaai.org/index.php/AAAI/article/view/21459
Fair representation learning of electronic health records +Dullerud, N., Roth, K., Hamidieh, K., Papernot, N. and Ghassemi, M., 2022. Is fairness only metric deep? evaluating and addressing subgroup gaps in deep metric learning. arXiv preprint arXiv:2203.12748. Reddy, C., Sharma, D., Mehri, S., Romero-Soriano, A., Shabanian, S. and Honari, S., 2021, June. Benchmarking bias mitigation algorithms in representation learning through fairness metrics. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1). Yuan, Y., Xun, G., Suo, Q., Jia, K. and Zhang, A., 2019. Wave2vec: Deep representation learning for clinical temporal data. Neurocomputing, 324, pp.31-42.
Federated Learning Aggregation Strategies by Weight Exploration +Li, Tian, et al. "Federated learning: Challenges, methods, and future directions." IEEE Signal Processing Magazine 37.3 (2020): 50-60. Kairouz, Peter, et al. "Advances and open problems in federated learning." Foundations and Trends® in Machine Learning 14.1–2 (2021): 1-210. Rieke, Nicola, et al. "The future of digital health with federated learning." NPJ digital medicine 3.1 (2020): 1-7. Li, Tian, et al. "Federated optimization in heterogeneous networks." Proceedings of Machine Learning and Systems 2 (2020): 429-450.
FirstResponse +Loreto Susperregi, et al. On the Use of a Low-Cost Thermal Sensor to Improve Kinect People Detection in a Mobile Robot. Sensors 2013, 13(11), 14687-14713. Simin Wang, Salim Zabir, Bastian Leibe. Lying Pose Recognition for Elderly Fall Detection. Proceedings of Robotics: Science and Systems 2011.
Forecast energy consumption in buildings to help Mestro customers save energy +https://www.youtube.com/watch?v=XAb7faAK7_0 https://www.youtube.com/watch?v=f_d8m8tHcMg
Forecasting Industrial IoT Time Series @AlfaLaval +https://www.youtube.com/watch?v=ZuydOEws92s
Foundation Models for Time Series Analysis +- Liang, Y., Wen, H., Nie, Y., Jiang, Y., Jin, M., Song, D., ... & Wen, Q. (2024, August). Foundation models for time series analysis: A tutorial and survey. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 6555-6565). - Rasul, K., Ashok, A., Williams, A. R., Khorasani, A., Adamopoulos, G., Bhagwatkar, R., ... & Rish, I. (2023). Lag-llama: Towards foundation models for time series forecasting. arXiv preprint arXiv:2310.08278. - Jin, M., Wang, S., Ma, L., Chu, Z., Zhang, J. Y., Shi, X., ... & Wen, Q. (2023). Time-llm: Time series forecasting by reprogramming large language models. arXiv preprint arXiv:2310.01728. - Liu, Y., Qin, G., Huang, X., Wang, J., & Long, M. (2024). Autotimes: Autoregressive time series forecasters via large language models. arXiv preprint arXiv:2402.02370. - Liu, X., Hu, J., Li, Y., Diao, S., Liang, Y., Hooi, B., & Zimmermann, R. (2024, May). Unitime: A language-empowered unified model for cross-domain time series forecasting. In Proceedings of the ACM on Web Conference 2024 (pp. 4095-4106). - Huang, X., Tang, J., & Shen, Y. (2024). Long time series of ocean wave prediction based on PatchTST model. Ocean Engineering, 301, 117572. - Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021, May). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 12, pp. 11106-11115). - Wu, H., Xu, J., Wang, J., & Long, M. (2021). Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting. Advances in neural information processing systems, 34, 22419-22430. - Wang, Y., Wu, H., Dong, J., Liu, Y., Qiu, Y., Zhang, H., ... & Long, M. (2024). Timexer: Empowering transformers for time series forecasting with exogenous variables. arXiv preprint arXiv:2402.19072.

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Generating synthetic time series data in case of data scarcity +- Using generative adversarial networks (GAN) to simulate central-place foraging trajectories https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13853 - Data-Driven Crowd Simulation with Generative Adversarial Networks http://rainbow-doc.irisa.fr/pdf/2019_amirian_casa.pdf
Generative Approach for Multivariate Signals +https://papers.nips.cc/paper/8789-time-series-generative-adversarial-networks.pdf https://openreview.net/pdf?id=Sy2fzU9gl https://www.sciencedirect.com/science/article/pii/S092658051930367X
Graph Neural Networks for Traffic Flow Forecasting +Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
Graph Neural Networks for cardiovascular disease +Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.

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Hide-and-Seek Privacy Challenge (NeurIPS 2020) +https://www.vanderschaar-lab.com/privacy-challenge/
Human Motion Analysis using Inertial Sensors +J. Rueterbories, E. G. Spaich, B. Larsen, and O. K. Andersen, “Methods for gait event detection and analysis in ambulatory systems,” Med. Eng. & Phys., vol. 32, no. 6, pp. 545–552, 2010. J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait & Posture, vol. 28, no. 1, pp. 1–15, 2008. D. Lai, R. Begg, and M. Palaniswami, “Computational intelligence in gait research: A perspective on current app. And future challenges,” Info. Tech. in Biomed., IEEE Trans. on, vol. 13, no. 5, pp. 687–702, 2009.
Human Value Detection +https://aclanthology.org/2022.acl-long.306.pdf https://www.youtube.com/watch?v=ZAQ4LELCCY4

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Identity verification of humans performing physical activities +Mohammad Derawi, Patrick Bours, Gait and activity recognition using commercial phones, Computers & Security, Volume 39, Part B, November 2013, Pages 137-144 Ailisto, Heikki J., et al. "Identifying people from gait pattern with accelerometers." Defense and Security. International Society for Optics and Photonics, 2005.
Image Retrieval for Arguments +https://touche.webis.de/clef22/touche22-web/image-retrieval-for-arguments.html
Improved networks for cloud-car communication +Generative Adverserial Nets Etschberger: CAN Controller Area Network - Grundlagen, Protokolle, Bausteine, Anwendungen
Improving MEDication Adherence through Person Centered Care and Adaptive Interventions +Definitions, variants, and causes of nonadherence with medication: a challenge for tailored interventions: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3711878/ Predictors of Medication Adherence Using a Multidimensional Adherence Model in Patients with Heart Failure: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2603618/ A machine learning approach for medication adherence monitoring using body-worn sensors: https://ieeexplore.ieee.org/document/7459425 Machine Learning Classification of Medication Adherence in Patients with Movement Disorders Using Non-Wearable Sensors: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5729888/
Intelligible patient representation for outcome prediction of congestive heart failure patients +1. Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records. Scientific Reports. 2016;6:26094. doi:10.1038/srep26094. 2. Choi, Edward, et al. "Multi-layer representation learning for medical concepts." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016
Interactive Anomaly Detection +- Lamba, H. and Akoglu, L., 2019, May. Learning On-the-Job to Re-rank Anomalies from Top-1 Feedback. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 612-620. Society for Industrial and Applied Mathematics. https://epubs.siam.org/doi/pdf/10.1137/1.9781611975673.69 - Ding, K., Li, J. and Liu, H., 2019, January. Interactive anomaly detection on attributed networks. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 357-365. ACM. http://www.public.asu.edu/~jundongl/paper/WSDM19_GraphUCB.pdf - Arnaldo, I., Veeramachaneni, K. and Lam, M., 2019. ex2: a framework for interactive anomaly detection. In ACM IUI Workshop on Exploratory Search and Interactive Data Analytics (ESIDA). http://ceur-ws.org/Vol-2327/IUI19WS-ESIDA-2.pdf - Zhu, Y. and Yang, K., 2019. Tripartite Active Learning for Interactive Anomaly Discovery. IEEE Access. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8707963
Investigating Robustness of DNNs +Szegedy, Christian, et al. "Intriguing properties of neural networks." arXiv preprint arXiv:1312.6199 (2013). Hinton, Geoffrey E., and Ruslan R. Salakhutdinov. "Reducing the dimensionality of data with neural networks." Science 313.5786 (2006): 504-507. Hinton, Geoffrey E. "Learning multiple layers of representation." Trends in cognitive sciences 11.10 (2007): 428-434. Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images." arXiv preprint arXiv:1412.1897 (2014). Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
Investigation of spread spectrum techniques to reduce the electromagnetic interference in switch mode power supply +(1) Gamoudi, Rabiaa, Dhia Elhak Chariag, and Lassaad Sbita. "A review of spread-spectrum-based PWM techniques—A novel fast digital implementation." IEEE Transactions on Power Electronics 33.12 (2018): 10292-10307. (2) Perotti, M., and F. Fiori. "Software based control of the EMI generated in BLDC motor drives." 2016 International Symposium on Electromagnetic Compatibility-EMC EUROPE. IEEE, 2016. (3) Blank, Mathias, et al. "Digital slew rate and s-shape control for smart power switches to reduce EMI generation." IEEE Transactions on Power Electronics 30.9 (2014): 5170-5180

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Knowledge graphs in healthcare +https://dl.acm.org/doi/10.1145/3447772 https://arxiv.org/abs/2306.04802

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Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation +Yang, Zhichao, et al. "Multi-label few-shot ICD coding as autoregressive generation with prompt." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 4. 2023. Liu, Leibo, et al. "Automated icd coding using extreme multi-label long text transformer-based models." Artificial Intelligence in Medicine (2023): 102662. Hu, Edward J., et al. "Lora: Low-rank adaptation of large language models." arXiv preprint arXiv:2106.09685 (2021). Sensoy, Murat, Lance Kaplan, and Melih Kandemir. "Evidential deep learning to quantify classification uncertainty." Advances in neural information processing systems 31 (2018).
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