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Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.

H. Chang, S. Vembu, S. Mohan, R. Uppaal, and A. McCallum. Mach. Learn., 109 (9-10): 1749-1778 (2020)

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Multi-resolution Networks For Flexible Irregular Time Series Modeling (Multi-FIT).B. Singh, I. Deznabi, B. Narasimhan, B. Kucharski, R. Uppaal, A. Josyula, and M. Fiterau. CoRR, (2019)Long Document Summarization in a Low Resource Setting using Pretrained Language Models.A. Bajaj, P. Dangati, K. Krishna, P. Kumar, R. Uppaal, B. Windsor, E. Brenner, D. Dotterrer, R. Das, and A. McCallum. ACL (student), page 71-80. Association for Computational Linguistics, (2021)Overcoming Practical Issues of Deep Active Learning and its Applications on Named Entity Recognition.H. Chang, S. Vembu, S. Mohan, R. Uppaal, and A. McCallum. CoRR, (2019)Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.H. Chang, S. Vembu, S. Mohan, R. Uppaal, and A. McCallum. Mach. Learn., 109 (9-10): 1749-1778 (2020)FEUDA: Frustratingly Easy Prompt Based Unsupervised Domain Adaptation.R. Uppaal, Y. Li, and J. Hu. CoRR, (2024)Evolving Domain Adaptation of Pretrained Language Models for Text Classification.Y. Chuang, Y. Wu, D. Gupta, R. Uppaal, A. Kumar, L. Sun, M. Sreedhar, S. Yang, T. Rogers, and J. Hu. CoRR, (2023)LRS-DAG: Low Resource Supervised Domain Adaptation with Generalization Across Domains.R. Uppaal. CoRR, (2019)Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detection.R. Uppaal, J. Hu, and Y. Li. ACL (1), page 12813-12832. Association for Computational Linguistics, (2023)

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Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.

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Disambiguation of "Uppaal, Rheeya"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.

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Using error decay prediction to overcome practical issues of deep active learning for named entity recognition.