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Graph neural networks (GNNs) have become a popular approach to integrating structural inductive biases into NLP models.
A value for n-person games
Lloyd S Shapley · 1953
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
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Dependency-based semantic role labeling of propbank
Richard Johansson and Pierre Nugues · 2008
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The CoNLL-2009 shared task: Syntactic and semantic dependencies in multiple languages
Jan Hajič, Massimiliano Ciaramita, Richard Johansson, Daisuke Kawahara, Maria Antònia Martí, Lluís Màrquez, Adam Meyers, Joakim Nivre, Sebastian Padó, Jan Štěpánek, Pavel Straňák, Mihai Surdeanu, Nianwen Xue, and Yi Zhang · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 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
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Interactive and interpretable machine learning models for human machine collaboration
Been Kim · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
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Neural semantic role labeling with dependency path embeddings
Michael Roth and Mirella Lapata · 2016
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Graph convolutional encoders for syntax-aware neural machine translation
Jasmijn Bastings, Ivan Titov, Wilker Aziz, Diego Marcheggiani, and Khalil Sima’an · 2017
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Categorical reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Encoding sentences with graph convolutional networks for semantic role labeling
Diego Marcheggiani and Ivan Titov · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S. Cohen, Tameem Adel, and Max Welling · 2017
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber · 2018
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Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, and Willem Zuidema · 2018
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Do language models understand anything? on the ability of LSTMs to understand negative polarity items
Jaap Jumelet and Dieuwke Hupkes · 2018
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Learning sparse neural networks through l 0 l_{0} regularization
Christos Louizos, Max Welling, and Diederik P. Kingma · 2018
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Interpretable graph convolutional neural networks for inference on noisy knowledge graphs
Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed, and Amir Saffari · 2018
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
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Graph-based dependency parsing with graph neural networks
Tao Ji, Yuanbin Wu, and Man Lan · 2019
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Text Generation from Knowledge Graphs with Graph Transformers
Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, Mirella Lapata, and Hannaneh Hajishirzi · 2019
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Abusive language detection with graph convolutional networks
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova · 2019
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Beyond word importance: Contextual decomposition to extract interactions from lstms
W. James Murdoch, Peter J Liu, and Bin Yu · 2019
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
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Weili Nie, Yang Zhang, and Ankit Patel · 2018
Cited alongside, same era.
Modeling relational data with graph convolutional networks
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling · 2018
Cited alongside, same era.
Modeling semantics with gated graph neural networks for knowledge base question answering
Daniil Sorokin and Iryna Gurevych · 2018
Cited alongside, same era.
Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William Cohen · 2018
Cited alongside, same era.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Cited alongside, same era.
Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel · 2018
Cited alongside, same era.
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
Tian Xie and Jeffrey C Grossman · 2018
Cited alongside, same era.
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Layerwise relevance visualization in convolutional text graph classifiers
Robert Schwarzenberg, Marc Hübner, David Harbecke, Christoph Alt, and Leonhard Hennig · 2019
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Is attention interpretable?
Sofia Serrano and Noah A. Smith · 2019
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Hierarchical interpretations for neural network predictions
Chandan Singh, W James Murdoch, and Bin Yu · 2019
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When explanations lie: Why modified bp attribution fails
Leon Sixt, Maximilian Granz, and Tim Landgraf · 2019
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Infomask: Masked variational latent representation to localize chest disease
Saeid Asgari Taghanaki, Mohammad Havaei, Tess Berthier, Francis Dutil, Lisa Di Jorio, Ghassan Hamarneh, and Yoshua Bengio · 2019
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Shangsheng Xie and Mingming Lu · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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GNNExplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Graph neural networks with generated parameters for relation extraction
Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun · 2019
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How do decisions emerge across layers in neural models? interpretation with differentiable masking
Nicola De Cao, Michael Schlichtkrull, Wilker Aziz, and Ivan Titov · 2020
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Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?
Alon Jacovi and Yoav Goldberg · 2020
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Towards Hierarchical Importance Attribution: Explaining Compositional Semantics for Neural Sequence Models
Xisen Jin, Junyi Du, Zhongyu Wei, Xiangyang Xue, and Xiang Ren · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Xai for graphs: Explaining graph neural network predictions by identifying relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2020
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Restricting the flow: Information bottlenecks for attribution
Karl Schulz, Leon Sixt, Federico Tombari, and Tim Landgraf · 2020
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Visualizing the impact of feature attribution baselines
Pascal Sturmfels, Scott Lundberg, and Su-In Lee · 2020
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