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Generative Adversarial Networks."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007736"},{"key":"e_1_3_2_1_17_1","volume-title":"ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. 2008 IEEE International Joint Conference on Neural Networks","author":"He Haibo","year":"2008","unstructured":"Haibo He and Yang Bai. 2008. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. 2008 IEEE International Joint Conference on Neural Networks (2008), 1322\u20131328."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118646106"},{"key":"e_1_3_2_1_19_1","unstructured":"Jonathan Ho Ajay Jain and Pieter Abbeel. 2020. 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Journal of Machine Learning Research 12 (2011)."},{"key":"e_1_3_2_1_41_1","volume-title":"Synthetic Data Applications in Finance. arXiv preprint arXiv:2401.00081","author":"Potluru K","year":"2023","unstructured":"Vamsi\u00a0K Potluru, Daniel Borrajo, Andrea Coletta, Niccol\u00f2 Dalmasso, Yousef El-Laham, Elizabeth Fons, Mohsen Ghassemi, Sriram Gopalakrishnan, Vikesh Gosai, Eleonora Krea\u010di\u0107, 2023. Synthetic Data Applications in Finance. arXiv preprint arXiv:2401.00081 (2023)."},{"key":"e_1_3_2_1_42_1","volume-title":"Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 397\u2013404","author":"Pradhan S","year":"2005","unstructured":"Sameer\u00a0S Pradhan, Edward Loper, Dmitriy Dligach, and Martha Palmer. 2005. Active Learning for Word Sense Disambiguation with Methods for Addressing the Class Imbalance Problem. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. 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LLaMA: Open and Efficient Foundation Language Models. arXiv preprint arXiv:2302.13971 (2023)."},{"key":"e_1_3_2_1_57_1","first-page":"2579","article-title":"Visualizing Data Using t-SNE","author":"Maaten Laurens Van\u00a0der","year":"2008","unstructured":"Laurens Van\u00a0der Maaten and Geoffrey Hinton. 2008. Visualizing Data Using t-SNE. Journal of Machine Learning Research 9, Nov (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_3_2_1_58_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention Is All You Need. In Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_2_1_59_1","volume-title":"Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence. IEEE, 647\u2013654","author":"Wang Shouhong","year":"2007","unstructured":"Shouhong Wang and Xin Yao. 2007. Improving Classification Performance on Imbalanced Data using Kernel-based Methods. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence. IEEE, 647\u2013654."},{"key":"e_1_3_2_1_60_1","volume-title":"Neural Vocoder: Neural Network-based Vocoding and Speech Synthesis. arXiv preprint arXiv:2304.07997","author":"Wang Zi","year":"2023","unstructured":"Zi Wang, Long Kang, and Shiyin Huang. 2023. Neural Vocoder: Neural Network-based Vocoding and Speech Synthesis. arXiv preprint arXiv:2304.07997 (2023)."},{"key":"e_1_3_2_1_61_1","first-page":"88","article-title":"Regularized Orthogonal Least Squares","volume":"23","author":"Wei Xu","year":"2003","unstructured":"Xu Wei and Stephen\u00a0A Billings. 2003. Regularized Orthogonal Least Squares. 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