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Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN

Published in , 2018

Recommended citation: Xiong, Wenting, et al. (2018). "Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN." NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy. https://arxiv.org/pdf/1811.03970.pdf

Efficient and Effective Training of Sparse Recurrent Neural Networks

Published in , 2021

Recommended citation: Liu, S., et al. (2021). "Efficient and Effective Training of Sparse Recurrent Neural Networks." Neural Computing and Applications. 33, 9625–9636.https://doi.org/10.1007/s00521-021-05727-y. https://link.springer.com/article/10.1007/s00521-021-05727-y

ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection

Published in , 2021

Recommended citation: Nimah, I., et al. (2021). "ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection." Findings of the Association for Computational Linguistics: EMNLP 2021. pages 1606–1617, Punta Cana, Dominican Republic. Association for Computational Linguistics. https://aclanthology.org/2021.findings-emnlp.138/

NLG Evaluation Beyond Correlation Analysis: An Empirical Metric Preference Checklist

Published in Conference, 2023

Recommended citation: Nimah, I., et al. (2023). "NLG Evaluation Beyond Correlation Analysis: An Empirical Metric Preference Checklist." In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1240–1266, Toronto, Canada. Association for Computational Linguistics.. https://aclanthology.org/2023.acl-long.69.pdf

Can BERT Learn Evidence-Aware Representation for Low Resource Fake News Detection?

Published in Conference, 2024

Recommended citation: Wijayanti, R. and Nimah, I. (2024). "Can BERT Learn Evidence-Aware Representation for Low Resource Fake News Detection?" In 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia.. https://doi.org/10.1109/IC3INA64086.2024.10732358

A Simple Contrastive Embedding Framework for Low-Resource Fake News Detection

Published in , 2025

Recommended citation: Nimah, I., et al. (2025). "A Simple Contrastive Embedding Framework for Low-Resource Fake News Detection." Neural Computing and Applications. https://doi.org/10.1007/s00521-025-11467-0. https://link.springer.com/article/10.1007/s00521-025-11467-0