Fetching the paper…
Reading the bibliography…
Interpreting black box classifiers, such as deep networks, allows an analyst to validate a classifier before it is deployed in a high-stakes setting.
Analysis of a complex of statistical variables into principal components
Hotelling, Harold · 1933
Earlier work this paper cites.
Extracting tree-structured representations of trained networks
Craven, Mark and Shavlik, Jude W · 1996
Earlier work this paper cites.
Are artificial neural networks black boxes?
Benítez, José Manuel, Castro, Juan Luis, and Requena, Ignacio · 1997
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Yann · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
Earlier work this paper cites.
Case-based explanation of non-case-based learning methods
Caruana, Rich, Kangarloo, Hooshang, Dionisio, JD, Sinha, Usha, and Johnson, David · 1999
Earlier work this paper cites.
Multidimensional scaling
Cox, Trevor F and Cox, Michael AA · 2000
Earlier work this paper cites.
From visual data exploration to visual data mining: a survey
De Oliveira, MC Ferreira and Levkowitz, Haim · 2003
Earlier work this paper cites.
Pattern recognition and machine learning
Bishop, Christopher M · 2006
Earlier work this paper cites.
Model compression
Bucilă, Cristian, Caruana, Rich, and Niculescu-Mizil, Alexandru · 2006
Earlier work this paper cites.
Visualizing data using t-sne
van der Maaten, Laurens and Hinton, Geoffrey · 2008
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Erhan, Dumitru, Bengio, Y, Courville, Aaron, and Vincent, Pascal · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, Alex and Hinton, Geoffrey · 2009
Earlier work this paper cites.
Learning a parametric embedding by preserving local structure
van der Maaten, Laurens · 2009
Earlier work this paper cites.
Dimensionality reduction: a comparative
van der Maaten, Laurens, Postma, Eric, and Van den Herik, Jaap · 2009
Earlier work this paper cites.
How to explain individual classification decisions
Baehrens, David, Schroeter, Timon, Harmeling, Stefan, Kawanabe, Motoaki, Hansen, Katja, and Müller, Klaus-Robert · 2010
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012
Cited alongside, same era.
Artificial neural networks in medical diagnosis
Amato, Filippo, López, Alberto, Peña-Méndez, Eladia MarÃa, VaÅhara, Petr, Hampl, AleÅ¡, and Havel, Josef · 2013
Cited alongside, same era.
Do deep nets really need to be deep?
Ba, Jimmy and Caruana, Rich · 2014
Cited alongside, same era.
Graph-Sparse LDA: A Topic Model with Structured Sparsity
Doshi-Velez, F., Wallace, B., and Adams, R · 2014
Cited alongside, same era.
t-sne visualization of cnn codes
Karpathy, Andrej · 2014
Cited alongside, same era.
Understanding deep image representations by inverting them
Mahendran, Aravindh and Vedaldi, Andrea · 2015
Later among the works it cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, Anh, Yosinski, Jason, and Clune, Jeff · 2015
Later among the works it cites.
Inceptionism: Going deeper into neural networks
Olah, Chris, Mordvintsev, Alexander, and Tyka, Mike · 2015
Later among the works it cites.
Distilling intractable generative models, 2015
Papamakarios, George and Murray, Iain · 2015
Later among the works it cites.
Uncertainty in Deep Learning
Gal, Yarin · 2016
Later among the works it cites.
The mythos of model interpretability
Lipton, Zachary C · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
The cifar-10 dataset
Krizhevsky, Alex, Nair, Vinod, and Hinton, Geoffrey · 2014
Cited alongside, same era.
Fitnets: Hints for thin deep nets
Romero, Adriana, Ballas, Nicolas, Kahou, Samira Ebrahimi, Chassang, Antoine, Gatta, Carlo, and Bengio, Yoshua · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, Karen and Zisserman, Andrew · 2014
Cited alongside, same era.
Accelerating t-sne using tree-based algorithms
van der Maaten, Laurens · 2014
Cited alongside, same era.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, Sebastian, Binder, Alexander, Montavon, Grégoire, Klauschen, Frederick, Müller, Klaus-Robert, and Samek, Wojciech · 2015
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, Rich, Lou, Yin, Gehrke, Johannes, Koch, Paul, Sturm, Marc, and Elhadad, Noemie · 2015
Cited alongside, same era.
”why should i trust you?”: Explaining the predictions of any classifier
Ribeiro, Marco Tulio, Singh, Sameer, and Guestrin, Carlos · 2016
Later among the works it cites.
Grad-cam: Why did you say that?
Selvaraju, Ramprasaath R, Das, Abhishek, Vedantam, Ramakrishna, Cogswell, Michael, Parikh, Devi, and Batra, Dhruv · 2016
Later among the works it cites.
Zagoruyko, Sergey and Komodakis, Nikos · 2016
Later among the works it cites.
Interpretability via model extraction
Bastani, Osbert, Kim, Carolyn, and Bastani, Hamsa · 2017
Later among the works it cites.
Towards a rigorous science of interpretable machine learning
Doshi-Velez, Finale and Kim, Been · 2017
Later among the works it cites.
Understanding Black-box Predictions via Influence Functions
Koh, P. W. and Liang, P · 2017
Later among the works it cites.
Efficient algorithms for t-distributed stochastic neighborhood embedding
Linderman, George C, Rachh, Manas, Hoskins, Jeremy G, Steinerberger, Stefan, and Kluger, Yuval · 2017
Later among the works it cites.
Feature visualization
Olah, Chris, Mordvintsev, Alexander, and Schubert, Ludwig · 2017
Later among the works it cites.
Smoothgrad: removing noise by adding noise
Smilkov, Daniel, Thorat, Nikhil, Kim, Been, Viégas, Fernanda, and Wattenberg, Martin · 2017
Later among the works it cites.