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Recurrent Neural Networks (RNNs) have achieved remarkable performance on a range of tasks.
Craven, Mark and Shavlik, Jude W, “Extracting tree-structured representations of trained networks”, NeurIPS, 1996
1996
Earlier work this paper cites.
Sato, Makoto and Tsukimoto, Hiroshi, “Rule extraction from neural networks via decision tree induction”, IJCNN, 2001
2001
Earlier work this paper cites.
Hinton, Geoffrey E, “Learning multiple layers of representation”, Trends in cognitive sciences, vol. 11, 2007
2007
Earlier work this paper cites.
Reynolds, Douglas A, “Gaussian Mixture Models", Encyclopedia of biometrics, 2009
2009
Earlier work this paper cites.
Xing, Zhengzheng and Pei, Jian and Keogh, Eamonn, “A brief survey on sequence classification”, ACM Sigkdd Explorations Newsletter, vol. 12, 2010
2010
Earlier work this paper cites.
Altmann, André and Toloşi, Laura and Sander, Oliver and Lengauer, Thomas, “Permutation importance: a corrected feature importance measure”, Bioinformatics, vol. 26, 2010
2010
Earlier work this paper cites.
Bengio, Yoshua and Delalleau, Olivier and Simard, Clarence, “Decision trees do not generalize to new variations”, Computational Intelligence, vol. 26, 2010
2010
Earlier work this paper cites.
Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E., “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, vol. 12, 2011
2011
Earlier work this paper cites.
Zeiler, Matthew D and Fergus, Rob, “Visualizing and understanding convolutional networks”, European conference on computer vision, 2014
2014
Earlier work this paper cites.
Bolei Zhou and Aditya Khosla and Àgata Lapedriza and Aude Oliva and Antonio Torralba, “Object Detectors Emerge in Deep Scene CNNs”, ICLR, 2015
2015
Earlier work this paper cites.
Johnson, Alistair EW and Pollard, Tom J and Shen, Lu and Li-wei, H Lehman and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Celi, Leo Anthony and Mark, Roger G, “MIMIC-III, a freely accessible critical care database”, Scientific data, vol. 3, 2016
2016
Earlier work this paper cites.
Luis M. Candanedo and Veronique Feldheim, “Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models”, Energy and Buildings, vol. 112, 2016
2016
Earlier work this paper cites.
Choi, Edward and Bahadori, Mohammad Taha and Sun, Jimeng and Kulas, Joshua and Schuetz, Andy and Stewart, Walter, “Retain: An interpretable predictive model for healthcare using reverse time attention mechanism”, NeurIPS, 2016
2016
Earlier work this paper cites.
Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos, “Why should i trust you?” Explaining the predictions of any classifier, ACM SIGKDD, 2016
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Tarwani, Kanchan M and Edem, Swathi, “Survey on recurrent neural network in natural language processing”, Int. J. Eng. Trends Technol, vol. 48, 2017
2017
Cited alongside, same era.
Gao, Lianli and Guo, Zhao and Zhang, Hanwang and Xu, Xing and Shen, Heng Tao, “Video captioning with attention-based LSTM and semantic consistency”, IEEE Transactions on Multimedia, vol. 19, 2017
2017
Cited alongside, same era.
Harutyunyan, Hrayr and Khachatrian, Hrant and Kale, David C. and Ver Steeg, Greg and Galstyan, Aram, “Multitask learning and benchmarking with clinical time series data”, Scientific Data, vol. 6, 2019
2019
Later among the works it cites.
Mark Ibrahim, Melissa Louie, Ceena Modarres, and John Paisley, “Global Explanations of Neural Networks: Mapping the Landscape of Predictions”, AAAI/ACM Conference on AI, Ethics, and Society, 2019
2019
Later among the works it cites.
Molnar, Christoph, “Interpretable machine learning”, 2019
2019
Later among the works it cites.
Ghorbani, Amirata and Wexler, James and Zou, James Y and Kim, Been, “Towards automatic concept-based explanations”, NeurIPS, 2019
2019
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
Chen, Daizhuo and Fraiberger, Samuel P and Moakler, Robert and Provost, Foster, “Enhancing transparency and control when drawing data-driven inferences about individuals”, Big data, 2017
2017
Cited alongside, same era.
Sarah Tan and Rich Caruana and Giles Hooker and Paul Koch and Albert Gordo, “Learning Global Additive Explanations for Neural Nets Using Model Distillation”, arXiv 1801.08640, 2018
2018
Cited alongside, same era.
Miotto, Riccardo and Wang, Fei and Wang, Shuang and Jiang, Xiaoqian and Dudley, Joel T, “Deep learning for healthcare: review, opportunities and challenges”, Briefings in bioinformatics, vol. 19, 2018
2018
Cited alongside, same era.
Zhang, Qingchen and Yang, Laurence T and Chen, Zhikui and Li, Peng, “A survey on deep learning for big data”, Information Fusion, vol. 42, 2018
2018
Cited alongside, same era.
Adebayo, Julius and Gilmer, Justin and Muelly, Michael and Goodfellow, Ian and Hardt, Moritz and Kim, Been, “Sanity checks for saliency maps”, NeurIPS, 2018
2018
Cited alongside, same era.
Bai, Tian and Zhang, Shanshan and Egleston, Brian L and Vucetic, Slobodan, “Interpretable representation learning for healthcare via capturing disease progression through time”, ACM SIGKDD, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
Weiss, Gail and Goldberg, Yoav and Yahav, Eran, “Learning Deterministic Weighted Automata with Queries and Counterexamples”, NeurIPS, 2019
2019
Later among the works it cites.
Du, Xiaoning and Xie, Xiaofei and Li, Yi and Ma, Lei and Liu, Yang and Zhao, Jianjun, “Deepstellar: model-based quantitative analysis of stateful deep learning systems”, ACM, 2019
2019
Later among the works it cites.
Sarah Wiegreffe and Yuval Pinter, “Attention is not not Explanation”, EMNLP-IJCNLP, 2019
2019
Later among the works it cites.
Fawaz, Hassan Ismail and Forestier, Germain and Weber, Jonathan and Idoumghar, Lhassane and Muller, Pierre-Alain, “Deep learning for time series classification: a review”, Data Mining and Knowledge Discovery, vol. 33, 2019
2019
Later among the works it cites.
Dimanov, Botty and Bhatt, Umang and Jamnik, Mateja and Weller, Adrian, “You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods”, 2020
2020
Closest in time.
Hou, Bo-Jian and Zhou, Zhi-Hua, “Learning With Interpretable Structure From Gated RNN”, IEEE, 2020
2020
Closest in time.
Sambaturu, Prathyush and Gupta, Aparna and Davidson, Ian and Ravi, SS and Vullikanti, Anil, and Warren, Andrew, “Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability”, AAAI, 2020
2020
Closest in time.
2020
Closest in time.
Yeh, Chih-Kuan and Kim, Been and Arik, Sercan and Li, Chun-Liang and Pfister, Tomas and Ravikumar, Pradeep, “On completeness-aware concept-based explanations in deep neural networks", NIPS, 2020
2020
Closest in time.