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Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood.
O. Pfungst, Clever Hans: (the horse of Mr. Von Osten.) a contribution to experimental animal and human psychology . Holt, Rinehart and Winston, 1911
1911
Earlier work this paper cites.
B. Fortner, “Hdf: The hierarchical data format,” Dr Dobb’s J Software Tools Prof Program , vol. 23, no. 5, p. 42, 1998
1998
Earlier work this paper cites.
M. Meila and J. Shi, “A random walks view of spectral segmentation,” in Proceedings of the International Workshop on Artificial Intelligence and Statistics (AISTATS) , 2001
2001
Earlier work this paper cites.
A. Y. Ng, M. I. Jordan, and Y. Weiss, “On spectral clustering: Analysis and an algorithm,” in Advances in Neural Information Processing Systems , 2002, pp. 849–856
2002
Earlier work this paper cites.
2007
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research , vol. 9, no. Nov, pp. 2579–2605, 2008
2008
Earlier work this paper cites.
J. Castro, D. Gómez, and J. Tejada, “Polynomial calculation of the shapley value based on sampling,” Comput. Oper. Res. , vol. 36, no. 5, pp. 1726–1730, 2009
2009
Earlier work this paper cites.
S. Marcel and Y. Rodriguez, “Torchvision the machine-vision package of torch,” in Proceedings of the International Conference on Multimedia (ACM Multimedia) . ACM, 2010, pp. 1485–1488
2010
Earlier work this paper cites.
E. Strumbelj and I. Kononenko, “An efficient explanation of individual classifications using game theory,” J. Mach. Learn. Res. , vol. 11, pp. 1–18, 2010
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. VanderPlas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in python,” J. Mach. Learn. Res. , vol. 12, pp. 2825–2830, 2011
2011
Earlier work this paper cites.
E. Bernhardsson, E. Freider, A. Rouhani, D. Buchfuhrer, G. Poulin, D. Stadther, U. Barbans, A. Kransnukhin, J. Crobak et al. , “Luigi,” 2012. [Online]. Available: https://github.com/spotify/luigi
2012
Earlier work this paper cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the 22nd ACM international conference on Multimedia , 2014, pp. 675–678
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in European conference on computer vision . Springer, 2014, pp. 818–833
2014
Earlier work this paper cites.
M. Grinberg, Flask Web Development - Developing Web Applications with Python . O’Reilly, 2014
2014
Earlier work this paper cites.
N. Jain, A. Bhansali, and D. Mehta, “Angularjs: A modern mvc framework in javascript,” Journal of Global Research in Computer Science , vol. 5, no. 12, pp. 17–23, 2014
2014
Earlier work this paper cites.
M. Beauchemin, K. Naik, J. Potiuk, K. Breguła, A. Berlin-Taylor, J. Cunningham, T. Urbaszek et al. , “Apache Airflow,” 2014. [Online]. Available: https://github.com/apache/airflow
2014
Earlier work this paper cites.
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller, and W. Samek, “On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation,” PLoS ONE , vol. 10, no. 7, p. e0130140, 2015
2015
Earlier work this paper cites.
F. Chollet et al. (2015) Keras. [Online]. Available: https://github.com/fchollet/keras
2015
Earlier work this paper cites.
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. A. Riedmiller, “Striving for simplicity: The all convolutional net,” in Proceedings of the International Conference of Learning Representations (ICLR) , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in Proceedings of the International Conference on Machine Learning, (ICML) , ser. JMLR Workshop and Conference Proceedings, vol. 37. JMLR.org, 2015, pp. 448–456
2015
Earlier work this paper cites.
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, “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek, “Analyzing classifiers: Fisher vectors and deep neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 2912–2920
2016
Earlier work this paper cites.
S. Lapuschkin, A. Binder, G. Montavon, K.-R. Müller, and W. Samek, “The LRP toolbox for artificial neural networks,” Journal of Machine Learning Research , vol. 17, pp. 114:1–114:5, 2016
2016
Earlier work this paper cites.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard et al. , “Tensorflow: A system for large-scale machine learning,” in { \{ USENIX } \} Symposium on Operating Systems Design and Implementation ( { \{ OSDI } \} 16) , 2016, pp. 265–283
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE Computer Society, 2016, pp. 770–778
2016
Earlier work this paper cites.
S. Zagoruyko and N. Komodakis, “Wide residual networks,” in Proceedings of the British Machine Vision Conference (BMVC) . BMVA Press, 2016
2016
Cited alongside, same era.
M. T. Ribeiro, S. Singh, and 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, San Francisco, CA, USA, August 13-17, 2016 , B. Krishnapuram, M. Shah, A. J. Smola, C. C. Aggarwal, D. Shen, and R. Rastogi, Eds. ACM, 2016, pp. 1135–1144
2016
Cited alongside, same era.
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller, “Explaining nonlinear classification decisions with deep taylor decomposition,” Pattern Recognition , vol. 65, pp. 211–222, 2017
2017
Cited alongside, same era.
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller, “Evaluating the visualization of what a deep neural network has learned,” IEEE Transactions on Neural Networks and Learning Systems , vol. 28, no. 11, pp. 2660–2673, 2017
2019
Later among the works it cites.
O. Dijk, R. Bell, A. Gädke, B. Serna, T. Okumus et al. , “Explainerdashboard,” 2019. [Online]. Available: https://github.com/oegedijk/explainerdashboard
2019
Later among the works it cites.
K. Dhamdhere, M. Sundararajan, and Q. Yan, “How important is a neuron,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019
2019
Later among the works it cites.
A. W. Senior, R. Evans, J. Jumper, J. Kirkpatrick, L. Sifre, T. Green, C. Qin, A. Zídek, A. W. R. Nelson, A. Bridgland, H. Penedones, S. Petersen, K. Simonyan, S. Crossan, P. Kohli, D. T. Jones, D. Silver, K. Kavukcuoglu, and D. Hassabis, “Improved protein structure prediction using potentials from deep learning,” Nature , vol. 577, no. 7792, pp. 706–710, 2020
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2017
Cited alongside, same era.
R. Okuta, Y. Unno, D. Nishino, S. Hido, and C. Loomis, “Cupy: A numpy-compatible library for nvidia gpu calculations,” in Proceedings of Workshop on Machine Learning Systems (LearningSys) in The Thirty-first Annual Conference on Neural Information Processing Systems (NIPS) , 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. PMLR, 2017, pp. 3319–3328
2017
Cited alongside, same era.
A. Shrikumar, P. Greenside, and A. Kundaje, “Learning important features through propagating activation differences,” in Proceedings of the 34th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 70. PMLR, 06–11 Aug 2017, pp. 3145–3153
2017
Cited alongside, same era.
S. M. Lundberg and S. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , I. Guyon, U. von Luxburg, S. Bengio, H. M. Wallach, R. Fergus, S. V. N. Vishwanathan, and R. Garnett, Eds., 2017, pp. 4765–4774
2017
Cited alongside, same era.
N. Pörner, H. Schütze, and B. Roth, “Evaluating neural network explanation methods using hybrid documents and morphosyntactic agreement,” in Proceedings of the Association for Computational Linguistics, (ACL) . Association for Computational Linguistics, 2018, pp. 340–350
2018
Cited alongside, same era.
J. Zhang, S. A. Bargal, Z. Lin, J. Brandt, X. Shen, and S. Sclaroff, “Top-down neural attention by excitation backprop,” International Journal of Computer Vision , vol. 126, no. 10, pp. 1084–1102, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
A. B. Arrieta, N. D. Rodríguez, J. D. Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-Lopez, D. Molina, R. Benjamins, R. Chatila, and F. Herrera, “Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI,” Inf. Fusion , vol. 58, pp. 82–115, 2020
2020
Later among the works it cites.
M. Kohlbrenner, A. Bauer, S. Nakajima, A. Binder, W. Samek, and S. Lapuschkin, “Towards best practice in explaining neural network decisions with lrp,” in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN) , 2020, pp. 1–7
2020
Later among the works it cites.
2020
Later among the works it cites.
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. Fernández del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature , vol. 585, p. 357–362, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” Int. J. Comput. Vis. , vol. 128, no. 2, pp. 336–359, 2020
2020
Later among the works it cites.
O. T. Unke, S. Chmiela, M. Gastegger, K. T. Schütt, H. E. Sauceda, and K.-R. Müller, “Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects,” Nature communications , vol. 12, no. 1, p. 7273, 2021
2021
Closest in time.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021
2021
Closest in time.
W. Samek, G. Montavon, S. Lapuschkin, C. J. Anders, and K.-R. Müller, “Explaining deep neural networks and beyond: A review of methods and applications,” Proceedings of the IEEE , vol. 109, no. 3, pp. 247–278, 2021
2021
Closest in time.
J. Aeles, F. Horst, S. Lapuschkin, L. Lacourpaille, and F. Hug, “Revealing the unique features of each individual’s muscle activation signatures,” Journal of the Royal Society Interface , vol. 18, no. 174, p. 20200770, 2021
2021
Closest in time.
S. Yeom, P. Seegerer, S. Lapuschkin, A. Binder, S. Wiedemann, K.-R. Müller, and W. Samek, “Pruning by explaining: A novel criterion for deep neural network pruning,” Pattern Recognition , vol. 115, p. 107899, 2021
2021
Closest in time.
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller, “A unifying review of deep and shallow anomaly detection,” Proceedings of the IEEE , vol. 109, no. 5, pp. 756–795, 2021
2021
Closest in time.
J. Klaise, A. V. Looveren, G. Vacanti, and A. Coca, “Alibi explain: Algorithms for explaining machine learning models,” J. Mach. Learn. Res. , vol. 22, pp. 181:1–181:7, 2021
2021
Closest in time.
A. Holzinger, A. Saranti, C. Molnar, P. Biece, and W. Samek, “Explainable ai methods - a brief overview,” in xxAI - Beyond Explainable AI , ser. Lecture Notes in Artificial Intelligence, A. Holzinger, R. Goebel, R. Fong, T. Moon, K.-R. Müller, and W. Samek, Eds., 2022, vol. 13200, pp. 13–38
2022
Closest in time.
L. Arras, A. Osman, and W. Samek, “Clevr-xai: A benchmark dataset for the ground truth evaluation of neural network explanations,” Information Fusion , vol. 81, pp. 14–40, 2022
2022
Closest in time.
C. J. Anders, L. Weber, D. Neumann, W. Samek, K.-R. Müller, and S. Lapuschkin, “Finding and removing clever hans: Using explanation methods to debug and improve deep models,” Information Fusion , vol. 77, pp. 261–295, 2022
2022
Closest in time.
C. Agarwal, S. Krishna, E. Saxena, M. Pawelczyk, N. Johnson, I. Puri, M. Zitnik, and H. Lakkaraju, “OpenXAI: Towards a transparent evaluation of model explanations,” in Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track , 2022
2022
Closest in time.
2022
Closest in time.
F. Motzkus, L. Weber, and S. Lapuschkin, “Measurably stronger explanation reliability via model canonization,” in 2022 IEEE International Conference on Image Processing (ICIP) . IEEE, 2022, pp. 516–520
2022
Closest in time.
2022
Closest in time.
A. Hedström, L. Weber, D. Krakowczyk, D. Bareeva, F. Motzkus, W. Samek, S. Lapuschkin, and M. M. M. Höhne, “Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond,” Journal of Machine Learning Research , vol. 24, no. 34, pp. 1–11, 2023
2023
Closest in time.