Tópicos em Gerenciamento de Dados e Informações: Minicursos do SBBD 2022

Altae-Tran, H., Ramsundar, B., Pappu, A. S., and Pande, V. (2017). Low data drug discovery with one-shot learning. ACS central science, 3(4):283–293.

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Artstein 2017 ARTSTEIN, R. Inter-annotator agreement. In: Handbook of linguistic annotation. [S.l.]: Springer, 2017. p. 297–313.

Atzeni, P., Bugiotti, F., and Rossi, L. (2014). Uniform Access to NoSQL Systems. Information Systems, 43(SI):117–133.

Aziz e Specia 2011 AZIZ, W.; SPECIA, L. Fully automatic compilation of a Portuguese-English parallel corpus for statistical machine translation. In: STIL 2011. Cuiabá, MT: [s.n.], 2011.

Bao, Y., Wu, M., Chang, S., and Barzilay, R. (2019). Few-shot text classification with distributional signatures. arXiv preprint arXiv:1908.06039.

Bick 2000 BICK, E. The Parsing System “Palavras”. Automatic Grammatical Analysis of Portuguese in a Constraint Grammar Framework. Århus: University of Arhus, 2000.

Bojanowski et al. 2016 BOJANOWSKI, P. et al. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016.

Brown et al. 2020 BROWN, T. et al. Language models are few-shot learners. In: LAROCHELLE, H. et al. (Ed.). Advances in Neural Information Processing Systems. Curran Associates, Inc., 2020. v. 33, p. 1877–1901. Disponível em: [link].

Chollet, F. (2021). Deep learning with Python. Simon and Schuster.

Demszky et al. 2020 DEMSZKY, D. et al. GoEmotions: A dataset of fine-grained emotions. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, 2020. p. 4040–4054. Disponível em: <https://aclanthology.org/2020.acl-main.372>.

Devlin et al. 2019 DEVLIN, J. et al. BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, Minnesota: Association for Computational Linguistics, 2019. p. 4171–4186. Disponível em: <https://aclanthology.org/N19-1423>.

Dong, N. and Xing, E. P. (2018). Few-Shot Semantic Segmentation with Prototype Learning. In BMVC, volume 3.

Dumoulin, V. and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285.

Elmasri, R. and Navathe, S. (2011). Fundamentals of Database Systems. Addison-Wesley, 6 edition.

Finn, C., Abbeel, P., and Levine, S. (2017). Model-agnostic metalearning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR.

Finn, C., Xu, K., and Levine, S. (2018). Probabilistic model-agnostic meta-learning. Advances in neural information processing systems, 31.

Frozza, A. and Mello, R. (2020). JS4Geo: A Canonical JSON Schema for Geographic Data Suitable to NoSQL Databases. GeoInf., 24(4):987–1019.

Garcia, V. and Bruna, J. (2017). Few-shot learning with graph neural networks. arXiv preprint arXiv:1711.04043.

Garnelo, M., Rosenbaum, D., Maddison, C., Ramalho, T., Saxton, D., Shanahan, M., Teh, Y. W., Rezende, D., and Eslami, S. A. (2018). Conditional neural processes. In International Conference on Machine Learning, pages 1704– 1713. PMLR.

Grant, E., Finn, C., Levine, S., Darrell, T., and Griffiths, T. (2018). Recasting gradient-based meta-learning as hierarchical bayes. arXiv preprint ar- Xiv:1801.08930.

Gu, J., Wang, Y., Chen, Y., Cho, K., and Li, V. O. (2018). Meta-learning for low-resource neural machine translation. arXiv preprint arXiv:1808.08437.

He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.

Henry Korth, S. S. and Silberschatz, A. (2019). Database System Concepts. McGraw-Hill Education, 7 edition.

Heuser, C. A. (2009). Projeto de Banco de Dados. Bookman, 6 edition.

Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780.

Hsu, J.-Y., Chen, Y.-J., and Lee, H.-y. (2020). Meta learning for end-to-end low-resource speech recognition. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 7844–7848. IEEE.

Huisman, M., Van Rijn, J. N., and Plaat, A. (2021). A survey of deep meta-learning. Artificial Intelligence Review, 54(6):4483–4541.

Joshi et al. 2020 JOSHI, M. et al. SpanBERT: Improving Pre-training by Representing and Predicting Spans. Transactions of the Association for Computational Linguistics, v. 8, p. 64–77, 01 2020. ISSN 2307-387X. Disponível em: <https://doi.org/10.1162/tacl_a_00300>.

Jurafsky e Martin 2021 JURAFSKY, D.; MARTIN, J. H. Speech and Language Processing. [s.n.], 2021. Draft of December 29, 2021. Disponível em: <https://web.stanford.edu/~jurafsky/>.

Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., and Darrell, T. (2019). Few-shot object detection via feature reweighting. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8420–8429.

Kilgarriff 1997 KILGARRIFF, A. I don’t believe in word senses. Computers and the Humanities, Springer, v. 31, n. 2, p. 91–113, 1997. ISSN 00104817.

Koch, G., Zemel, R., Salakhutdinov, R., et al. (2015). Siamese neural networks for one-shot image recognition. In ICML deep learning workshop, volume 2, page 0. Lille.

Lake, B. M., Ullman, T. D., Tenenbaum, J. B., and Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and brain sciences, 40.

Li, Z., Zhou, F., Chen, F., and Li, H. (2017). Meta-sgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835.

Lima, C. (2016). Projeto Lógico de Bancos de Dados NoSQL Documento a Partir de Esquemas Conceituais Entidade-Relacionamento Estendido (EER). Master’s thesis, PPGCC-UFSC.

Lima, C. and Mello, R. (2015). A Workload-driven Logical Design Approach for NoSQL Document Databases. In XVII ACM iiWAS, pages 73:1–73:10.

Lima, C. and Mello, R. (2016). On Proposing and Evaluating a NoSQL Document Database Logical Design Approach. Int. J. Web Inf. Syst., 12(4):398–417.

Liu et al. 2019 LIU, Y. et al. RoBERTa: A Robustly Optimized BERT Pretraining Approach. 2019. Cite arxiv:1907.11692. Disponível em: <http://arxiv.org/abs/1907.11692>.

Maicas, G., Bradley, A. P., Nascimento, J. C., Reid, I., and Carneiro, G. (2018). Training medical image analysis systems like radiologists. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 546–554. Springer.

McShane e Nirenburg 2021 MCSHANE, M.; NIRENBURG, S. Linguistics for the Age of AI. The MIT Press, 2021. ISBN 9780262363136. Disponível em: <https://doi.org/10.7551/mitpress/13618.001.0001>.

Mikolov et al. 2013 MIKOLOV, T. et al. Efficient estimation of word representations in vector space. Proceedings of Workshop at ICLR, v. 2013, 01 2013.

Mirikharaji, Z., Yan, Y., and Hamarneh, G. (2019). Learning to segment skin lesions from noisy annotations. In Domain adaptation and representation transfer and medical image learning with less labels and imperfect data, pages 207– 215. Springer.

Mishra, N., Rohaninejad, M., Chen, X., and Abbeel, P. (2017). A simple neural attentive meta-learner. arXiv preprint arXiv:1707.03141.

Munkhdalai, T. and Yu, H. (2017). Meta networks. In International Conference on Machine Learning, pages 2554–2563. PMLR.

Neves Oliveira, B. S., do Rêgo, L. G. C., Peres, L., da Silva, T. L. C., and de Macêdo, J. A. F. (2022a). Processamento de linguagem natural via aprendizagem profunda. Sociedade Brasileira de Computação.

Neves Oliveira, B. S., do Rêgo, L. G. C., Peres, L., da Silva, T. L. C., and de Macêdo, J. A. F. (2022b). Processamento de Linguagem Natural via Aprendizagem Profunda. Sociedade Brasileira de Computação.

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Oliveira et al. 2015 OLIVEIRA, H. G. et al. As wordnets do português. Oslo Studies in Language, v. 7, n. 1, p. 397–424, 2015.

Panagopoulos, G., Nikolentzos, G., and Vazirgiannis, M. (2021). Transfer graph neural networks for pandemic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 4838–4845.

Papineni et al. 2002 PAPINENI, K. et al. Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. Philadelphia, Pennsylvania, USA: Association for Computational Linguistics, 2002. p. 311–318. Disponível em: <https://aclanthology.org/P02-1040>.

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Sadalage, P. J. and Fowler, M. (2013). NoSQL Distilled : A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley.

Salem, F. K. A., Al Feel, R., Elbassuoni, S., Ghannam, H., Jaber, M., and Farah, M. (2021). Meta-learning for fake news detection surrounding the syrian war. Patterns, 2(11):100369.

Santana, L. H. Z. and Mello, R. (2020). An Analysis of Mapping Strategies for Storing RDF Data into NoSQL Databases. In XXXV ACM SAC, pages 386–392.

Santos et al. 2010 Relações semânticas em português: comparando o TeP, o MWN.PT, o Port4NooJ e o PAPEL. [S.l.]: Associação Portuguesa de Linguística: Lisboa, 2010. 681–700 p.

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Schreiner, G. A., Duarte, D., and Mello, R. (2015). SQLtoKey- NoSQL: A Layer for Relational to Key-based NoSQL Database Mapping. In XVII ACM iiWAS, pages 74:1–74:9. ACM.

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Schreiner, G., Duarte, D., and Mello, R. (2020). Bringing SQL Databases to Key-based NoSQL Databases: A Canonical Approach. Computing, 102(1):221–246.

Silva, T. H. V. and Mello, R. (2021). A Rule-based Conversion of an EER Schema to Neo4j Schema Constraints. In XXXVI SBBD, pages 181–192.

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Souza et al. 2020 SOUZA, F.; NOGUEIRA, R.; LOTUFO, R. BERTimbau: Pretrained BERT Models for Brazilian Portuguese. In: CERRI, R.; PRATI, R. C. (Ed.). Intelligent Systems. Cham: Springer International Publishing, 2020. p. 403–417. ISBN 978-3-030-61377-8.

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Visca, M., Powell, R., Gao, Y., and Fallah, S. (2022). Deep metalearning energy-aware path planner for unmanned ground vehicles in unknown terrains. IEEE Access, 10:30055–30068.

Wang, C., Zhu, Y., Liu, H., Zang, T., Yu, J., and Tang, F. (2022). Deep meta-learning in recommendation systems: A survey. arXiv preprint ar-Xiv:2206.04415.

Wang, T.-C., Liu, M.-Y., Tao, A., Liu, G., Kautz, J., and Catanzaro, B. (2019). Few-shot video-to-video synthesis. arXiv preprint arXiv:1910.12713.

Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., and Ahn, S. (2018). Bayesian model-agnostic meta-learning. Advances in neural information processing systems, 31.

Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C. (2020). Gradient surgery for multi-task learning. Advances in Neural Information Processing Systems, 33:5824–5836.

Zakharov, E., Shysheya, A., Burkov, E., and Lempitsky, V. (2019). Few-shot adversarial learning of realistic neural talking head models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9459–9468.

Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J. (2021). Dive into Deep Learning. arXiv preprint arXiv:2106.11342.