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In this paper, we propose a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network.
R. Luss, P. Chen, A. Dhurandhar, P. Sattigeri, K. Shanmugam, C. Tu, Generating contrastive explanations with monotonic attribute functions, CoRR abs/1905.12698 (2019) · 1905
Earlier work this paper cites.
D. L. Ruderman, The statistics of natural images, Network: Computation in Neural Systems (1994)
1994
Earlier work this paper cites.
H. Lee, A. Battle, R. Raina, A. Y. Ng, Efficient sparse coding algorithms, Advances in neural information processing systems 19 (2007) 801
2007
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, S. Belongie, The caltech-ucsd birds-200-2011 dataset, Tech. Rep. CNS-TR-2011-001, California Institute of Technology (2011)
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks, in: Advances in Neural Information Processing Systems, 2012, pp. 1097–1105
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. D. Zeiler, R. Fergus, Visualizing and understanding convolutional networks, in: European conference on computer vision, Springer, 2014, pp. 818–833
2014
Earlier work this paper cites.
K. Simonyan, A. Vedaldi, A. Zisserman, Deep inside convolutional networks: Visualising image classification models and saliency maps, in: ICLR Workshop, 2014
2014
Earlier work this paper cites.
R. Kiros, R. Salakhutdinov, R. S. Zemel, Multimodal neural language models., in: Icml, Vol. 14, 2014, pp. 595–603
2014
Earlier work this paper cites.
C. Kong, D. Lin, M. Bansal, R. Urtasun, S. Fidler, What are you talking about? text-to-image coreference, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3558–3565
2014
Earlier work this paper cites.
D. Lin, S. Fidler, C. Kong, R. Urtasun, Visual semantic search: Retrieving videos via complex textual queries, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2657–2664
2014
Earlier work this paper cites.
P. Agrawal, R. Girshick, J. Malik, Analyzing the performance of multilayer neural networks for object recognition, in: European Conference on Computer Vision, Springer, 2014, pp. 329–344
2014
Earlier work this paper cites.
J. Ba, R. Caruana, Do deep nets really need to be deep?, in: Advances in Neural Information Processing Systems, 2014
2014
Earlier work this paper cites.
C. Cao, X. Liu, Y. Yang, Y. Yu, J. Wang, Z. Wang, Y. Huang, L. Wang, C. Huang, W. Xu, et al., Look and think twice: Capturing top-down visual attention with feedback convolutional neural networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2956–2964
2015
Earlier work this paper cites.
R. Caruana, Y. Lou, J. Gehrke, P. Koch, M. Sturm, N. Elhadad, Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission, in: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, 2015, pp. 1721–1730
2015
Earlier work this paper cites.
B. Letham, C. Rudin, T. H. McCormick, D. Madigan, Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model, The Annals of Applied Statistics 9 (2015) 1350–1371
2015
Earlier work this paper cites.
T. Kulesza, M. Burnett, W.-K. Wong, S. Stumpf, Principles of explanatory debugging to personalize interactive machine learning, in: Proceedings of the 20th International Conference on Intelligent User Interfaces, ACM, 2015, pp. 126–137
2015
Earlier work this paper cites.
A. Karpathy, L. Fei-Fei, Deep visual-semantic alignments for generating image descriptions, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
2015
Earlier work this paper cites.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K. Müller, W. Samek, On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation, PLoS ONE 10 (2015)
2015
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Object detectors emerge in deep scene cnns, in: ICLR, 2015
2015
Earlier work this paper cites.
M. Simon, E. Rodner, Neural activation constellations: Unsupervised part model discovery with convolutional networks, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 1143–1151
2015
Earlier work this paper cites.
T. Xiao, Y. Xu, K. Yang, J. Zhang, Y. Peng, Z. Zhang, The application of two-level attention models in deep convolutional neural network for fine-grained image classification, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 842–850
2015
Earlier work this paper cites.
G. Gkioxari, R. Girshick, J. Malik, Actions and attributes from wholes and parts, in: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2470–2478
2015
Earlier work this paper cites.
W. Chen, J. Wilson, S. Tyree, K. Weinberger, Y. Chen, Compressing neural networks with the hashing trick, in: International Conference on Machine Learning, 2015, pp. 2285–2294
2015
Earlier work this paper cites.
M. Simon, E. Rodner, Neural activation constellations: Unsupervised part model discovery with convolutional networks, in: International Conference on Computer Vision (ICCV), 2015
2015
Earlier work this paper cites.
P. A. Burrough, R. McDonnell, R. A. McDonnell, C. D. Lloyd, Principles of Geographical Information Systems, Vol. 8.11 Nearest neighbours: Thiessen (Dirichlet/Voroni) polygons, Oxford University Press, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: IEEE Conference on Computer Vision and Pattern Recognition, 2016
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. L. adn Koray Kavukcuoglu, T. Graepel, D. Hassabis, Mastering the game of go with deep neural networks and tree search, Nature 529 (2016) 484–489
2016
Earlier work this paper cites.
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba, Learning deep features for discriminative localization, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 2921–2929
2016
Cited alongside, same era.
J. Zhang, Z. Lin, J. Brandt, X. Shen, S. Sclaroff, Top-down neural attention by excitation backprop, in: European Conference on Computer Vision, Springer, 2016, pp. 543–559
2016
Cited alongside, same era.
2016
Cited alongside, same era.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, D. Batra, Grad-cam: Visual explanations from deep networks via gradient-based localization, arXiv Preprint:1610.02391 (2016)
2016
Cited alongside, same era.
J. O. M., K. Wang, T. Tuytelaars, Visual explanation by interpretation: Improving visual feedback capabilities of deep neural networks , CoRR abs/1712.06302 (2017) · 2017
Closest in time.
Z. Qi, F. Li, Learning explainable embeddings for deep networks, in: NIPS 2017 workshop: Interpreting, Explaining and Visualizing Deep Learning - now what?, 2017
2017
Closest in time.
F. Doshi-Velez, B. Kim, Towards a rigorous science of interpretable machine learning, arXiv: Machine Learning (2017)
2017
Closest in time.
2017
Closest in time.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, A. Torralba, Places: A 10 million image database for scene recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
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B. Ustun, C. Rudin, Supersparse linear integer models for optimized medical scoring systems, Machine Learning 102 (3) (2016) 349–391
2016
Cited alongside, same era.
L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele, T. Darrell, Generating visual explanations, in: European Conference on Computer Vision, 2016
2016
Cited alongside, same era.
D. H. Park, L. A. Hendricks, Z. Akata, B. Schiele, T. Darrell, M. Rohrbach, Attentive explanations: Justifying decisions and pointing to the evidence, arXiv Preprint:1612.04757 (2016)
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, C. Guestrin, 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, 2016, pp. 1135–1144
2016
Cited alongside, same era.
A. Jain, A. R. Zamir, S. Savarese, A. Saxena, Structural-rnn: Deep learning on spatio-temporal graphs, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5308–5317
2016
Cited alongside, same era.
Z. Che, S. Purushotham, R. Khemani, Y. Liu, Interpretable deep models for icu outcome prediction, in: American Medical Informatics Association Annual Symposium, 2016
2016
Cited alongside, same era.
H. Zhang, T. Xu, M. Elhoseiny, X. Huang, S. Zhang, A. Elgammal, D. Metaxas, Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 1143–1152
2016
Cited alongside, same era.
Y. Zhang, X.-S. Wei, J. Wu, J. Cai, J. Lu, V.-A. Nguyen, M. N. Do, Weakly supervised fine-grained categorization with part-based image representation, IEEE Transactions on Image Processing 25 (4) (2016) 1713–1725
2016
Cited alongside, same era.
2017
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B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, A. Torralba, Scene parsing through ade20k dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017
2017
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B. Zhou, Y. Sun, D. Bau, A. Torralba, Interpretable basis decomposition for visual explanation, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018
2018
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V. Petsiuk, A. Das, K. Saenko, Rise: Randomized input sampling for explanation of black-box models, in: Proceedings of the British Machine Vision Conference (BMVC), 2018
2018
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M. T. Ribeiro, S. Singh, C. Guestrin, Anchors: High-precision model-agnostic explanations, in: AAAI, 2018
2018
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Y. Wang, H. Su, B. Zhang, X. Hu, Interpret neural networks by identifying critical data routing paths, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
2018
Closest in time.
Q. Zhang, Y. N. Wu, S. Zhu, Interpretable convolutional neural networks, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8827–8836
2018
Closest in time.
B. Kim, M. Wattenberg, J. Gilmer, C. J. Cai, J. Wexler, F. Viégas, R. Sayres, Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav), in: ICML, 2018
2018
Closest in time.
O. Li, H. Liu, C. Chen, C. Rudin, Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions, in: AAAI, 2018, pp. 3530–3537
2018
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D. Alvarez-Melis, T. S. Jaakkola, Towards robust interpretability with self-explaining neural networks, in: NIPS, 2018, pp. 7786–7795
2018
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S. Tan, R. Caruana, G. Hooker, P. Koch, A. Gordo, Learning Global Additive Explanations for Neural Nets Using Model Distillation, arXiv e-prints (Jan. 2018) · 2018
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A. Kumar, P. Sattigeri, A. Balakrishnan, Variational inference of disentangled latent concepts from unlabeled observations, in: ICLR 2018, 2018
2018
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A. Shih, A. Choi, A. Darwiche, A symbolic approach to explaining bayesian network classifiers, in: IJCAI, 2018, pp. 5103–5111
2018
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Z. C. Lipton, The mythos of model interpretability, Queue 16 (2018) 31 – 57
2018
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C. Rudin, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nature Machine Intelligence 1 (2018) 206–215
2018
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2018
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S. Wiegreffe, Y. Pinter, Attention is not not explanation, in: EMNLP, 2019
2019
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C. Chen, O. Li, A. Barnett, J. Su, C. Rudin, This looks like that: deep learning for interpretable image recognition, in: NeurIPS, 2019
2019
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A. Ignatiev, N. Narodytska, J. Marques-Silva, Abduction-based explanations for machine learning models, in: AAAI, 2019
2019
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C. Molnar, Interpretable Machine Learning, 2019
2019
Closest in time.
M. Riedl, Human-centered artificial intelligence and machine learning, ArXiv abs/1901.11184 (2019)
2019
Closest in time.
doi:10.1002/widm.1312
A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. Müller, Causability and explainabilty of artificial intelligence in medicine, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9 (2019) e1312 · 2019
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K. Sokol, P. Flach, Explainability fact sheets: a framework for systematic assessment of explainable approaches, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (2020)
2020
Closest in time.
doi:10.1007/s13218-020-00636-z
A. Holzinger, A. Carrington, H. Müller, Measuring the quality of explanations: The system causability scale (scs): Comparing human and machine explanations, KI - Künstliche Intelligenz 34 (01 2020) · 2020
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