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A Survey of Deep Active Learning [article]

Pengzhen Ren, Yun Xiao, Xiaojun Chang, Po-Yao Huang, Zhihui Li, Brij B. Gupta, Xiaojiang Chen, Xin Wang
<span title="2021-12-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves.  ...  Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples.  ...  In Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006 (ACM International Conference Proceeding Series, Vol. 148  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2009.00236v2">arXiv:2009.00236v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zuk2doushzhlfaufcyhoktxj7e">fatcat:zuk2doushzhlfaufcyhoktxj7e</a> </span>
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On the Effectiveness of Iterative Learning Control [article]

Anirudh Vemula, Wen Sun, Maxim Likhachev, J. Andrew Bagnell
<span title="2021-12-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications.  ...  However, there is little prior theoretical work that explains the effectiveness of ILC even in the presence of large modeling errors, where optimal control methods using the misspecified model (MM) often  ...  Moore, editors, Machine Learning, Proceedings of the Twenty-Third International Conference (ICML 2006), Pittsburgh, Pennsylvania, USA, June 25-29, 2006, volume 148 of ACM International Conference  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.09434v3">arXiv:2111.09434v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lwnqcrx4wneiddqrgwq2dpke4q">fatcat:lwnqcrx4wneiddqrgwq2dpke4q</a> </span>
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GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation

Derek Chen, Zhou Yu
<span title="">2021</span> <i title="Association for Computational Linguistics"> Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing </i> &nbsp; <span class="release-stage">unpublished</span>
To tackle this limited-data problem, previous methods focus on better modeling the distribution of in-scope (INS) examples.  ...  We also analyze the unique properties of OOS data to identify key factors for optimally applying our proposed method. 1  ...  Acknowledgments The authors are grateful to Tao Lei, Yi Yang, Jason Wu and Samuel R. Bowman for reviewing earlier versions of the manuscript.  ... 
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