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Journal of Documentation 28: 11-21
Jones KS (1972) A statistical interpretation of term specificity and its application in retrieval · 1972
Earlier work this paper cites.
Communications of the ACM 18: 613-620
Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing · 1975
Earlier work this paper cites.
Nature 323: 533-536
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors · 1986
Earlier work this paper cites.
Neural Processing Letters 2: 1-4
Dimopoulos Y, Bourret P, Lek S (1995) Use of some sensitivity criteria for choosing networks with good generalization ability · 1995
Earlier work this paper cites.
Journal of Machine Learning Research (JMLR) 3: 1137-1155
Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model · 2003
Earlier work this paper cites.
Ecological Modelling 160: 249-264
Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in artificial neural network models · 2003
Earlier work this paper cites.
Nadeau C, Bengio Y (2003) Inference for the generalization error · 2003
Earlier work this paper cites.
In: Proceedings of the 18th Conference on Innovative Applications of Artificial Intelligence (IAAI). AAAI Press, pp. 1822-1829
Poulin B, Eisner R, Szafron D, Lu P, Greiner R, et al. (2006) Visual explanation of evidence in additive classifiers · 2006
Earlier work this paper cites.
In: Proceedings of the International Conference on Machine Learning (ICML). pp. 641-648
Mnih A, Hinton G (2007) Three new graphical models for statistical language modelling · 2007
Earlier work this paper cites.
In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING). pp. 365-373
Hasan KS, Ng V (2010) Conundrums in unsupervised keyphrase extraction : making sense of the state-of-the-art · 2010
Earlier work this paper cites.
Journal of Machine Learning Research (JMLR) 11: 1803-1831
Baehrens D, Schroeter T, Harmeling S, Kawanabe M, Hansen K, et al. (2010) How to explain individual classification decisions · 2010
Earlier work this paper cites.
Journal of Machine Learning Research (JMLR) 11: 1-18
Strumbelj E, Kononenko I (2010) An efficient explanation of individual classifications using game theory · 2010
Earlier work this paper cites.
Journal of Machine Learning Research (JMLR) 12: 2493-2537
Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, et al. (2011) Natural language processing (almost) from scratch · 2011
Cited alongside, same era.
In: Aggarwal CC, Zhai C, editors, Mining Text Data. Springer, pp. 163-222
Aggarwal CC, Zhai C (2012) A survey of text classification algorithms · 2012
Cited alongside, same era.
In: Proceedings of the International Conference on Machine Learning (ICML). pp. 1751-1758
Mnih A, Teh YW (2012) A fast and simple algorithm for training neural probabilistic language models · 2012
Cited alongside, same era.
In: Advances in Neural Information Processing Systems 26 (NIPS)
Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality · 2013
Cited alongside, same era.
In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, pp. 1631-1642
Socher R, Perelygin A, Wu J, Chuang J, Manning CD, et al. (2013) Recursive deep models for semantic compositionality over a sentiment treebank · 2013
In: Advances in Neural Information Processing Systems 28 (NIPS)
Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification · 2015
Later among the works it cites.
arXiv 1609.08259
Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2016) Quantum-chemical insights from deep tensor neural networks · 2016
Closest in time.
Pattern Recognition
Montavon G, Bach S, Binder A, Samek W, Müller KR (2016) Explaining nonlinear classification decisions with deep taylor decomposition · 2016
Closest in time.
IEEE Transactions on Neural Networks and Learning Systems
Samek W, Binder A, Montavon G, Lapuschkin S, Müller KR (2016) Evaluating the visualization of what a deep neural network has learned · 2016
Closest in time.
In: Pattern Recognition - 38th German Conference, GCPR 2016, Springer, volume 9796 of
Arbabzadah F, Montavon G, Müller KR, Samek W (2016) Identifying individual facial expressions by deconstructing a neural network · 2016
Closest in time.
Journal of Neuroscience Methods 274: 141-145
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Cited alongside, same era.
In: IEEE Symposium on Computational Intelligence and Data Mining (CIDM). pp. 32-38
Landecker W, Thomure MD, Bettencourt LMA, Mitchell M, Kenyon GT, et al. (2013) Interpreting individual classifications of hierarchical networks · 2013
Cited alongside, same era.
In: International Conference on Learning Representations Workshop (ICLR)
Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space · 2013
Cited alongside, same era.
In: Computer Vision – ECCV 2014: 13th European Conference. pp. 818-833
Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks · 2014
Cited alongside, same era.
In: International Conference on Learning Representations Workshop (ICLR)
Simonyan K, Vedaldi A, Zisserman A (2014) Deep inside convolutional networks: visualising image classification models and saliency maps · 2014
Cited alongside, same era.
Technical report, University of Oxford
Denil M, Demiraj A, de Freitas N (2014) Extraction of salient sentences from labelled documents · 2014
Cited alongside, same era.
In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 1746-1751
Kim Y (2014) Convolutional neural networks for sentence classification · 2014
Cited alongside, same era.
PLoS ONE 10: e0130140
Bach S, Binder A, Montavon G, Klauschen F, Müller KR, et al. (2015) On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation · 2015
Cited alongside, same era.
Sturm I, Lapuschkin S, Samek W, Müller KR (2016) Interpretable deep neural networks for single-trial eeg classification · 2016
Closest in time.
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2912-2920
Lapuschkin S, Binder A, Montavon G, Müller KR, Samek W (2016) Analyzing classifiers: Fisher vectors and deep neural networks · 2016
Closest in time.
In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Ribeiro MT, Singh S, Guestrin C (2016) ”why should i trust you?”: explaining the predictions of any classifier · 2016
Closest in time.
In: Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). pp. 681-691
Li J, Chen X, Hovy E, Jurafsky D (2016) Visualizing and understanding neural models in nlp · 2016
Closest in time.
In: Proceedings of the 1st Workshop on Representation Learning for NLP. Association for Computational Linguistics, pp. 1-7
Arras L, Horn F, Montavon G, Müller KR, Samek W (2016) Explaining predictions of non-linear classifiers in nlp · 2016
Closest in time.
Journal of Machine Learning Research 17: 1-5
Lapuschkin S, Binder A, Montavon G, Müller KR, Samek W (2016) The layer-wise relevance propagation toolbox for artificial neural networks · 2016
Closest in time.