Fetching the paper…
Reading the bibliography…
Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision.
Simplifying decision trees
J. R. Quinlan · 1987
Earlier work this paper cites.
Generalized linear models
P. McCullagh and J. A. Nelder · 1989
Earlier work this paper cites.
Human issues in the use of pattern recognition techniques
A. Dix · 1992
Earlier work this paper cites.
Permutation importance: a corrected feature importance measure
A. Altmann, L. Toloşi, O. Sander, and T. Lengauer · 2010
Earlier work this paper cites.
Comprehensible classification models: a position paper
A. A. Freitas · 2014
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek · 2015
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
Earlier work this paper cites.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, and N. Elhadad · 2015
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
Earlier work this paper cites.
Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
Earlier work this paper cites.
Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
Cited alongside, same era.
Deep learning
I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio · 2016
Cited alongside, same era.
Visualizing and understanding recurrent networks
A. Karpathy, J. Johnson, and L. Fei-Fei · 2016
Cited alongside, same era.
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
Cited alongside, same era.
Multifaceted feature visualization: Uncovering the different types of features learned by each neuron in deep neural networks
A. Nguyen, J. Yosinski, and J. Clune · 2016
Cited alongside, same era.
Why should i trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
S. Wachter, B. Mittelstadt, and C. Russell · 2017
Later among the works it cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Oztireli, and M. Gross · 2018
Closest in time.
Towards explanation of dnn-based prediction with guided feature inversion
M. Du, N. Liu, Q. Song, and X. Hu · 2018
Closest in time.
Adversarial detection with model interpretation
N. Liu, H. Yang, and X. Hu · 2018
Closest in time.
Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2018
Closest in time.
Interpretable Machine Learning
C. Molnar · 2018
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Genesim: genetic extraction of a single, interpretable model
G. Vandewiele, O. Janssens, F. Ongenae, F. De Turck, and S. Van Hoecke · 2016
Cited alongside, same era.
Interpretability via model extraction
O. Bastani, C. Kim, and H. Bastani · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
P. Dabkowski and Y. Gal · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning
F. Doshi-Velez and B. Kim · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
R. Fong and A. Vedaldi · 2017
Cited alongside, same era.
Representation of linguistic form and function in recurrent neural networks
A. Kádár, G. Chrupała, and A. Alishahi · 2017
Cited alongside, same era.
Did the model understand the question?
P. K. Mudrakarta, A. Taly, M. Sundararajan, and K. Dhamdhere · 2018
Closest in time.
Deep contextualized word representations
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer · 2018
Closest in time.
Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
Closest in time.
Interpretable to whom? a role-based model for analyzing interpretable machine learning systems
R. Tomsett, D. Braines, D. Harborne, A. Preece, and S. Chakraborty · 2018
Closest in time.
Interpretable convolutional neural networks
Q. Zhang, Y. N. Wu, and S.-C. Zhu · 2018
Closest in time.
On attribution of recurrent neural network predictions via additive decomposition
M. Du, N. Liu, F. Yang, and X. Hu · 2019
Closest in time.
Representation interpretation with spatial encoding and multimodal analytics
N. Liu, M. Du, and X. Hu · 2019
Closest in time.