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With the broader and highly successful usage of machine learning in industry and the sciences, there has been a growing demand for Explainable AI.
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Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
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Kernel k-means: spectral clustering and normalized cuts
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Gaussian Processes for Machine Learning
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Toward Brain-Computer Interfacing
G. Dornhege, J. d. R. Millán, T. Hinterberger, D. McFarland, K.-R. Müller, et al · 2007
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Kernel PCA for novelty detection
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller · 2007
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Supervised group lasso with applications to microarray data analysis
S. Ma, X. Song, and J. Huang · 2007
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Optimizing spatial filters for robust eeg single-trial analysis
B. Blankertz, R. Tomioka, S. Lemm, M. Kawanabe, and K.-R. Müller · 2008
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Polynomial calculation of the shapley value based on sampling
J. Castro, D. Gómez, and J. Tejada · 2009
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Invited commentary: causal diagrams and measurement bias
M. A. Hernán and S. R. Cole · 2009
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The graph neural network model
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
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How to explain individual classification decisions
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller · 2010
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Learning to combine foveal glimpses with a third-order Boltzmann machine
H. Larochelle and G. E. Hinton · 2010
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Learning local features for age estimation on real-life faces
C. Shan · 2010
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An efficient explanation of individual classifications using game theory
E. Strumbelj and I. Kononenko · 2010
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Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
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Visual interpretation of kernel-based prediction models
K. Hansen, D. Baehrens, T. Schroeter, M. Rupp, and K.-R. Müller · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Efficient backprop
Y. A. LeCun, L. Bottou, G. B. Orr, and K.-R. Müller · 2012
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The Taylor decomposition: A unified generalization of the Oaxaca method to nonlinear models
S. Bazen and X. Joutard · 2013
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Interpreting individual classifications of hierarchical networks
W. Landecker, M. D. Thomure, L. M. A. Bettencourt, M. Mitchell, G. T. Kenyon, and S. P. Brumby · 2013
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Learning to relate images
R. Memisevic · 2013
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Age and gender estimation of unfiltered faces
E. Eidinger, R. Enbar, and T. Hassner · 2014
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A survey on concept drift adaptation
J. Gama, I. Zliobaite, A. Bifet, M. Pechenizkiy, and A. Bouchachia · 2014
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On the interpretation of weight vectors of linear models in multivariate neuroimaging
S. Haufe, F. C. Meinecke, K. Görgen, S. Dähne, J. Haynes, B. Blankertz, and F. Bießmann · 2014
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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Network in network
M. Lin, Q. Chen, and S. Yan · 2014
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On the number of linear regions of deep neural networks
G. F. Montúfar, R. Pascanu, K. Cho, and Y. Bengio · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Batch effect confounding leads to strong bias in performance estimates obtained by cross-validation
C. Soneson, S. Gerster, and M. Delorenzi · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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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
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Neural machine translation by jointly learning to align and translate
D. Bahdanau, K. Cho, and Y. Bengio · 2015
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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
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Machine learning applications in cancer prognosis and prediction
K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis · 2015
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Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. A. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Inceptionism: Going deeper into neural networks
A. Mordvintsev, C. Olah, and M. Tyka · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. M. Nguyen, J. Yosinski, and J. Clune · 2015
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Dex: Deep expectation of apparent age from a single image
R. Rothe, R. Timofte, and L. Van Gool · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. S. Bernstein, A. C. Berg, and F. Li · 2015
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Deep learning in neural networks: An overview
J. Schmidhuber · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller · 2015
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Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms
M. M.-C. Vidovic, N. Görnitz, K.-R. Müller, G. Rätsch, and M. Kloft · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. C. Courville, R. Salakhutdinov, R. S. Zemel, and Y. Bengio · 2015
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Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Is robustness the cost of accuracy? - A comprehensive study on the robustness of 18 deep image classification models
D. Su, H. Zhang, H. Chen, J. Yi, P. Chen, and Y. Gao · 2018
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Detecting statistical interactions from neural network weights
M. Tsang, D. Cheng, and Y. Liu · 2018
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Explaining therapy predictions with layer-wise relevance propagation in neural networks
Y. Yang, V. Tresp, M. Wunderle, and P. A. Fasching · 2018
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Top-down neural attention by excitation backprop
J. Zhang, Z. Bargal, Sarah Adeland Lin, J. Brandt, X. Shen, and S. Sclaroff · 2018
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Interpreting deep visual representations via network dissection
B. Zhou, D. Bau, A. Oliva, and A. Torralba · 2018
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iNNvestigate neural networks!
M. Alber, S. Lapuschkin, P. Seegerer, M. Hägele, K. T. Schütt, G. Montavon, W. Samek, K.-R. Müller, S. Dähne, and P.-J. Kindermans · 2019
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Generating visual explanations
L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, and T. Darrell · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and <1mb model size
F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J. Dally, and K. Keutzer · 2016
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Classifying and segmenting microscopy images with deep multiple instance learning
O. Z. Kraus, L. J. Ba, and B. J. Frey · 2016
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Analyzing classifiers: Fisher vectors and deep neural networks
S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek · 2016
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The layer-wise relevance propagation toolbox for artificial neural networks
S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. M. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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Explaining deep neural networks with a polynomial time algorithm for shapley value approximation
M. Ancona, C. Oztireli, and M. Gross · 2019
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Understanding patch-based learning of video data by explaining predictions
C. J. Anders, G. Montavon, W. Samek, and K.-R. Müller · 2019
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Finding and removing Clever Hans: Using explanation methods to debug and improve deep models
C. J. Anders, L. Weber, D. Neumann, W. Samek, K.-R. Müller, and S. Lapuschkin · 2019
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Rudder: Return decomposition for delayed rewards
J. A. Arjona-Medina, M. Gillhofer, M. Widrich, T. Unterthiner, J. Brandstetter, and S. Hochreiter · 2019
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Explaining and interpreting lstms
L. Arras, J. A. Arjona-Medina, M. Widrich, G. Montavon, M. Gillhofer, K.-R. Müller, S. Hochreiter, and W. Samek · 2019
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GAN dissection: Visualizing and understanding generative adversarial networks
D. Bau, J. Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, and A. Torralba · 2019
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
W. Brendel and M. Bethge · 2019
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Explanations can be manipulated and geometry is to blame
A. Dombrowski, M. Alber, C. J. Anders, M. Ackermann, K.-R. Müller, and P. Kessel · 2019
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Understanding deep networks via extremal perturbations and smooth masks
R. Fong, M. Patrick, and A. Vedaldi · 2019
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Producing radiologist-quality reports for interpretable deep learning
W. Gale, L. Oakden-Rayner, G. Carneiro, L. J. Palmer, and A. P. Bradley · 2019
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Contextual prediction difference analysis
J. Gu and V. Tresp · 2019
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A survey of methods for explaining black box models
R. Guidotti, A. Monreale, S. Ruggieri, F. Turini, F. Giannotti, and D. Pedreschi · 2019
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Fooling neural network interpretations via adversarial model manipulation
J. Heo, S. Joo, and T. Moon · 2019
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Causability and explainability of artificial intelligence in medicine
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, and H. Müller · 2019
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Interpretable text-to-image synthesis with hierarchical semantic layout generation
S. Hong, D. Yang, J. Choi, and H. Lee · 2019
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A benchmark for interpretability methods in deep neural networks
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2019
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Explaining the unique nature of individual gait patterns with deep learning
F. Horst, S. Lapuschkin, W. Samek, K.-R. Müller, and W. I. Schöllhorn · 2019
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Explaining convolutional neural networks using softmax gradient layer-wise relevance propagation
B. K. Iwana, R. Kuroki, and S. Uchida · 2019
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From clustering to cluster explanations via neural networks
J. Kauffmann, M. Esders, G. Montavon, W. Samek, and K.-R. Müller · 2019
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Pytorch Captum
N. Kokhlikyan, V. Miglani, M. Martin, E. Wang, J. Reynolds, A. Melnikov, N. Lunova, and O. Reblitz-Richardson · 2019
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Neuralhydrology - interpreting lstms in hydrology
F. Kratzert, M. Herrnegger, D. Klotz, S. Hochreiter, and G. Klambauer · 2019
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Unmasking Clever Hans predictors and assessing what machines really learn
S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K.-R. Müller · 2019
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Towards explainable NLP: A generative explanation framework for text classification
H. Liu, Q. Yin, and W. Y. Wang · 2019
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A rate-distortion framework for explaining neural network decisions
J. MacDonald, S. Wäldchen, S. Hauch, and G. Kutyniok · 2019
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Making the black box more transparent: Understanding the physical implications of machine learning
A. McGovern, R. Lagerquist, D. J. Gagne, G. E. Jergensen, K. L. Elmore, C. R. Homeyer, and T. Smith · 2019
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Explanation in artificial intelligence: Insights from the social sciences
T. Miller · 2019
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Gradient-based vs. propagation-based explanations: An axiomatic comparison
G. Montavon · 2019
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Layer-wise relevance propagation: An overview
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller · 2019
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Interpretable deep learning in drug discovery
K. Preuer, G. Klambauer, F. Rippmann, S. Hochreiter, and T. Unterthiner · 2019
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Structuring neural networks for more explainable predictions
L. Rieger, P. Chormai, G. Montavon, L. Hansen, and K.-R. Müller · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Müller, editors · 2019
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Quantum-chemical insights from interpretable atomistic neural networks
K. T. Schütt, M. Gastegger, A. Tkatchenko, and K.-R. Müller · 2019
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Analyzing neuroimaging data through recurrent deep learning models
A. W. Thomas, H. R. Heekeren, K.-R. Müller, and W. Samek · 2019
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Actionable recourse in linear classification
B. Ustun, A. Spangher, and Y. Liu · 2019
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Don’t paint it black: White-box explanations for deep learning in computer security
A. Warnecke, D. Arp, C. Wressnegger, and K. Rieck · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec · 2019
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Deep neural network or dermatologist?
K. Young, G. Booth, B. Simpson, R. Dutton, and S. Shrapnel · 2019
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Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
Z. Zhang, P. Chen, M. McGough, F. Xing, C. Wang, M. Bui, Y. Xie, M. Sapkota, L. Cui, J. Dhillon, et al · 2019
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Galaxy morphology classification with deep convolutional neural networks
X.-P. Zhu, J.-M. Dai, C.-J. Bian, Y. Chen, S. Chen, and C. Hu · 2019
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Explaining image classifiers by removing input features using generative models
C. Agarwal and A. Nguyen · 2020
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Ground truth evaluation of neural network explanations with clevr-xai
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Understanding the role of individual units in a deep neural network
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Learning global pairwise interactions with bayesian neural networks
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Building and interpreting deep similarity models
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Evaluation, tuning, and interpretation of neural networks for working with images in meteorological applications
I. Ebert-Uphoff and K. Hilburn · 2020
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Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
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Explaining explanations: Axiomatic feature interactions for deep networks
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Towards explaining anomalies: A deep Taylor decomposition of one-class models
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Towards best practice in explaining neural network decisions with LRP
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Explainable artificial intelligence model to predict acute critical illness from electronic health records
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Applying machine learning methods to detect convection using goes-16 abi data
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Fully interpretable deep learning model of transcriptional control
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Estimating pm2. 5 concentration of the conterminous united states via interpretable convolutional neural networks
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Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
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R. Roscher, B. Bohn, M. F. Duarte, and J. Garcke · 2020
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Higher-order explanations of graph neural networks via relevant walks
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The whole is more than its parts? from explicit to implicit pose normalization
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Visualizing the impact of feature attribution baselines
P. Sturmfels, S. Lundberg, and S.-I. Lee · 2020
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Extracting an explanatory graph to interpret a cnn
Q. Zhang, X. Wang, R. Cao, Y. N. Wu, F. Shi, and S.-C. Zhu · 2020
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Morphological and molecular breast cancer profiling through explainable machine learning
A. Binder, M. Bockmayr, M. Hägele, S. Wienert, D. Heim, K. Hellweg, A. Stenzinger, L. Parlow, J. Budczies, B. Goeppert, D. Treue, M. Kotani, M. Ishii, M. Dietel, A. Hocke, C. Denkert, K.-R. Müller, and F. Klauschen · 2021
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A unifying review of deep and shallow anomaly detection
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