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Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain.
Shangsheng Xie and Mingming Lu. 2019 · 1903
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Gnn explainer: A tool for post-hoc explanation of graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 1903
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour. 2019 · 1905
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. 2015 · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
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”What is relevant in a text document?”: An interpretable machine learning approach
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2017 · 2017
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Pubmed 200k rct: a dataset for sequential sentence classification in medical abstracts
Franck Dernoncourt and Ji Young Lee. 2017 · 2017
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Explaining nonlinear classification decisions with deep taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller. 2017 · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017 · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller. 2018 · 2018
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Scispacy: Fast and robust models for biomedical natural language processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 2019
Closest in time.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. 2019 · 2019
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Simplifying graph convolutional network
Felix Wu, Tianyi Zhan, Amauri Holonda de Souza Jr., Christopher Fifty, Tao Yu, and Kilian Weinberger Q. 2019 · 2019
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Learning convolutional neural networks for graphs
Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016 · 2023
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Advances in pre-training distributed word representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018 · 2018
Cited alongside, same era.