| 1 |
@article{domingues2024painel,
title={Painel de vigil{\^a}ncia da sa{\'u}de materna: ferramenta para vigil{\^a}ncia epidemiol{\'o}gica},
author={Domingues, R. M. S. M. and others},
journal={Rev. Bras. Epidemiol.},
volume={27},
pages={e240009},
year={2024}
}
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| 2 |
@inproceedings{10.1145/3448016.3452762,
author = {Glenis, Apostolos and Koutrika, Georgia},
title = {PyExplore: Query Recommendations for Data Exploration without Query Logs},
year = {2021},
isbn = {9781450383431},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3448016.3452762},
doi = {10.1145/3448016.3452762},
abstract = {Helping users explore data becomes increasingly more important as databases get larger and more complex. In this demo, we present PyExplore, a data exploration tool aimed at helping end users formulate queries over new datasets. PyExplore takes as input an initial query from the user along with some parameters and provides interesting queries by leveraging data correlations and diversity.},
booktitle = {Proceedings of the 2021 International Conference on Management of Data},
pages = {2731–2735},
numpages = {5},
keywords = {query recommendations, data exploration, clustering},
location = {Virtual Event, China},
series = {SIGMOD '21}
}
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| 3 |
@misc{han2025metadataextractionleveraginglarge,
title={Metadata Extraction Leveraging Large Language Models},
author={Cuize Han and Sesh Jalagam},
year={2025},
eprint={2510.19334},
archivePrefix={arXiv},
primaryClass={stat.ML},
howpublished={arXiv preprint arXiv:2510.19334.},
}
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| 4 |
@inproceedings{llmdap2025,
title={Llmdap: Llm-based data profiling and sharing},
author={Jiang, Shanshan and S{\o}rb{\o}, Sondre and Tinn, Phil and Karim, Shang Ferheng and Roman, Dumitru},
booktitle={VLDB 2025 Workshop: 3rd Data EConomy Workshop (DEC)},
year={2025}
}
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| 5 |
@INPROCEEDINGS{SOM_toward_brain_inspired,
author={Khacef, Lyes and Miramond, Benoît and Barrientos, Diego and Upegui, Andres},
booktitle={2019 International Joint Conference on Neural Networks (IJCNN)},
title={Self-organizing neurons: toward brain-inspired unsupervised learning},
year={2019},
volume={},
number={},
pages={1-9},
keywords={Neurons;Self-organizing feature maps;Training;Unsupervised learning;Biological neural networks;Labeling;Synapses;brain-inspired computing;self-organizing maps;unsupervised learning;embedded image classification},
doi={10.1109/IJCNN.2019.8852098}}
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| 6 |
@misc{Files,
author = {Allan Lima and Rui Filho and Samuel Silva and Denis Martins and Fernando Neto},
title = {Prompt de entrada para EsqueMapa},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.18299520},
howpublished = {\url{https://doi.org/10.5281/zenodo.18299520}},
}
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| 7 |
@inproceedings{pinheiro2025smartapsus,
title={SmartApSUS: Integrando Dados, Previsao e Otimiza{\c{c}}ao para Fortalecer a Gestao da APS no Brasil},
author={Pinheiro, T. and others},
booktitle={Proc. SBBD},
pages={82--87},
year={2025},
organization={SBC}
}
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| 8 |
@inproceedings{Renze_2024,
title={The Effect of Sampling Temperature on Problem Solving in Large Language Models},
DOI={10.18653/v1/2024.findings-emnlp.432},
booktitle={Findings of the Association for Computational Linguistics: EMNLP 2024},
publisher={Association for Computational Linguistics},
author={Renze, Matthew},
year={2024},
pages={7346–7356} }
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| 9 |
@inproceedings{vesanto2000clustering,
author = {Vesanto, J. and Alhoniemi, E.},
title = {Clustering of the self-organizing map},
booktitle = {IEEE Trans. Neural Networks},
year = {2000},
volume = {11},
number = {3},
pages = {586--600}
}
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