Fetching the paper…
Reading the bibliography…
Although recurrent neural networks (RNNs) are state-of-the-art in numerous sequential decision-making tasks, there has been little research on explaining their predictions.
A value for n-person games
Lloyd S Shapley · 1953
Earlier work this paper cites.
Monotonic solutions of cooperative games
H. Peyton Young · 1985
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Untersuchungen zu dynamischen neuronalen netzen
Sepp Hochreiter · 1991
Earlier work this paper cites.
The problem of learning long-term dependencies in recurrent networks
Yoshua Bengio, Paolo Frasconi, and Patrice Simard · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Consumer vulnerability to fraud: Influencing factors
Jinkook Lee and Horacio Soberon-Ferrer · 1997
Earlier work this paper cites.
Coefficient of Variation
Charles E. Brown · 1998
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
Earlier work this paper cites.
Extraction of salient sentences from labelled documents
Misha Denil, Alban Demiraj, and Nando De Freitas · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Matthew D. Zeiler and Rob Fergus · 2014
Earlier work this paper cites.
On the properties of neural machine translation: encoder–decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Explaining prediction models and individual predictions with feature contributions
Erik Štrumbelj and Igor Kononenko · 2014
Cited alongside, same era.
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
Cited alongside, same era.
Understanding neural networks through representation erasure
Jiwei Li, Will Monroe, and Dan Jurafsky · 2016
Cited alongside, same era.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart · 2016
Cited alongside, same era.
Interpretable predictions of clinical outcomes with an attention-based recurrent neural network
Ying Sha and May D. Wang · 2017
Later among the works it cites.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Later among the works it cites.
LSTMVis: A tool for visual analysis of hidden state dynamics in recurrent neural networks
Hendrik Strobelt, Sebastian Gehrmann, Hanspeter Pfister, and Alexander M. Rush · 2018
Later among the works it cites.
Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record
Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, and Laura E. Barnes · 2018
Later among the works it cites.
A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti, and Dino Pedreschi · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
"Why should I trust you?": Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Cited alongside, same era.
MIMIC-III, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-Wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
Cited alongside, same era.
Fraud detection system: A survey
Aisha Abdallah, Mohd Aizaini Maarof, and Anazida Zainal · 2016
Cited alongside, same era.
Explaining nonlinear classification decisions with deep Taylor decomposition
Grégoire Montavon, Sebastian Lapuschkin, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Automatic rule extraction from long short term memory networks
W. James Murdoch and Arthur Szlam · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M. Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Aya Abdelsalam Ismail, Mohamed Gunady, Luiz Pessoa, Hector Corrada Bravo, and Soheil Feizi · 2019
Later among the works it cites.
Is attention interpretable?
Sofia Serrano and Noah A. Smith · 2019
Later among the works it cites.
Attention is not explanation
Sarthak Jain and Byron C. Wallace · 2019
Later among the works it cites.
Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
Later among the works it cites.
Identity fraud study: Genesis of the identity fraud crisis
Krista Tedder and John Buzzar · 2020
Closest in time.
Interpreting a recurrent neural network’s predictions of ICU mortality risk
Long V. Ho, Melissa Aczon, David Ledbetter, and Randall Wetzel · 2021
Closest in time.
Credit card fraud
Federal Bureau of Investigation · 2021
Closest in time.