Fetching the paper…
Reading the bibliography…
The decision-making process of many state-of-the-art machine learning models is inherently inscrutable to the extent that it is impossible for a human to interpret the model directly: they are black box models.
Faithfully explaining rankings in a news recommender system
Maartje ter Hoeve, Anne Schuth, Daan Odijk, and Maarten de Rijke. 2018 · 1907
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Opinion mining and sentiment analysis
Bo Pang and Lillian Lee. 2008 · 2008
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems . 1097–1105
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
Earlier work this paper cites.
Generative adversarial nets. In Advances in neural information processing systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
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
Earlier work this paper cites.
From group to individual labels using deep features. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 597–606
Dimitrios Kotzias, Misha Denil, Nando De Freitas, and Padhraic Smyth. 2015 · 2015
Cited alongside, same era.
An unexpected unity among methods for interpreting model predictions
Scott Lundberg and Su-In Lee. 2016 · 2016
Cited alongside, same era.
Model-agnostic interpretability of machine learning
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016a · 2016
Cited alongside, same era.
LSTM: A search space odyssey
Klaus Greff, Rupesh K Srivastava, Jan Koutník, Bas R Steunebrink, and Jürgen Schmidhuber. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems . 4768–4777
Scott Lundberg and Su-In Lee. 2017 · 2017
Meaningful information and the right to explanation
Andrew D Selbst and Julia Powles. 2017 · 2017
Later among the works it cites.
Learning important features through propagating activation differences. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 3145–3153
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje. 2017 · 2017
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 · 2018
Later among the works it cites.
Brent Mittelstadt, Chris Russell, and Sandra Wachter. 2018 · 2018
Later among the works it cites.
Glove: Global vectors for word representation. 2014
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2018 · 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…
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller. 2017 · 2017
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 1135–1144
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016b
Cited in the paper.
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. 2019 · 2019
Closest in time.