Fetching the paper…
Reading the bibliography…
A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions.
L. S. Shapley, “17. a value for n-person games,” in Contributions to the Theory of Games (AM-28), Volume II . Princeton University Press, 1953
1953
Earlier work this paper cites.
J. Von Neumann, “Probabilistic logics and the synthesis of reliable organisms from unreliable components,” Automata studies , vol. 34, pp. 43–98, 1956
1956
Earlier work this paper cites.
D. H. Hubel and T. N. Wiesel, “Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex,” The Journal of Physiology , vol. 160, no. 1, pp. 106–154, Jan. 1962
1962
Earlier work this paper cites.
J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics . Berkeley, Calif.: University of California Press, 1967, pp. 281–297
1967
Earlier work this paper cites.
J. C. Gower and G. J. S. Ross, “Minimum spanning trees and single linkage cluster analysis,” Applied Statistics , vol. 18, no. 1, p. 54, 1969
1969
Earlier work this paper cites.
A. K. Jain and R. C. Dubes, Algorithms for Clustering Data . Upper Saddle River, NJ, USA: Prentice-Hall, Inc., 1988
1988
Earlier work this paper cites.
W. R. Swartout and J. D. Moore, Explanation in Second Generation Expert Systems . Berlin, Heidelberg: Springer-Verlag, 1993, p. 543–585
1993
Earlier work this paper cites.
J. M. Zurada, A. Malinowski, and I. Cloete, “Sensitivity analysis for minimization of input data dimension for feedforward neural network,” in IEEE International Symposium on Circuits and Systems , 1994, pp. 447–450
1994
Earlier work this paper cites.
M. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining , 1996, pp. 226–231
1996
Earlier work this paper cites.
T. Joachims, “A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization,” in Proceedings of the Fourteenth International Conference on Machine Learning , 1997, pp. 143–151
1997
Earlier work this paper cites.
A. K. Jain, M. N. Murty, and P. J. Flynn, “Data clustering: A review,” ACM Comput. Surv. , vol. 31, no. 3, pp. 264–323, 1999
1999
Earlier work this paper cites.
S. Tavazoie, J. D. Hughes, M. J. Campbell, R. J. Cho, and G. M. Church, “Systematic determination of genetic network architecture,” Nature Genetics , vol. 22, no. 3, pp. 281–285, Jul. 1999
1999
Earlier work this paper cites.
B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A. J. Smola, “Input space versus feature space in kernel-based methods,” IEEE Trans. Neural Networks , vol. 10, no. 5, pp. 1000–1017, 1999
1999
Earlier work this paper cites.
J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 22, no. 8, pp. 888–905, 2000
2000
Earlier work this paper cites.
M. Meila and J. Shi, “Learning segmentation by random walks,” in Advances in Neural Information Processing Systems 13 , 2000, pp. 873–879
2000
Earlier work this paper cites.
M. Halkidi, Y. Batistakis, and M. Vazirgiannis, “On clustering validation techniques,” J. Intell. Inf. Syst. , vol. 17, no. 2-3, pp. 107–145, 2001
2001
Earlier work this paper cites.
K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, and B. Schölkopf, “An introduction to kernel-based learning algorithms,” IEEE transactions on neural networks , vol. 12, no. 2, pp. 181–201, 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 14 , 2001, pp. 849–856
2001
Earlier work this paper cites.
B. Schölkopf and A. J. Smola, Learning with Kernels: support vector machines, regularization, optimization, and beyond . MIT Press, 2002
2002
Earlier work this paper cites.
R. Zhang and A. I. Rudnicky, “A large scale clustering scheme for kernel k-means,” in 16th International Conference on Pattern Recognition , 2002, pp. 289–292
2002
Earlier work this paper cites.
A. K. Kau, Y. E. Tang, and S. Ghose, “Typology of online shoppers,” Journal of Consumer Marketing , vol. 20, no. 2, pp. 139–156, Apr. 2003
2003
Earlier work this paper cites.
D. Jiang, C. Tang, and A. Zhang, “Cluster analysis for gene expression data: A survey,” IEEE Trans. Knowl. Data Eng. , vol. 16, no. 11, pp. 1370–1386, 2004
2004
Earlier work this paper cites.
I. S. Dhillon, Y. Guan, and B. Kulis, “Kernel k-means: spectral clustering and normalized cuts,” in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 2004, pp. 551–556
2004
Earlier work this paper cites.
J. G. Dy and C. E. Brodley, “Feature selection for unsupervised learning,” J. Mach. Learn. Res. , vol. 5, pp. 845–889, 2004
2004
Earlier work this paper cites.
M. H. C. Law, M. A. T. Figueiredo, and A. K. Jain, “Simultaneous feature selection and clustering using mixture models,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 26, no. 9, pp. 1154–1166, 2004
2004
Earlier work this paper cites.
T. Lange, V. Roth, M. L. Braun, and J. M. Buhmann, “Stability-based validation of clustering solutions,” Neural Computation , vol. 16, no. 6, pp. 1299–1323, 2004
2004
Earlier work this paper cites.
R. Xu and D. C. W. II, “Survey of clustering algorithms,” IEEE Trans. Neural Networks , vol. 16, no. 3, pp. 645–678, 2005
2005
Earlier work this paper cites.
C. Schäfer and J. Laub, “Annealed κ \kappa -means clustering and decision trees,” in Classification—the Ubiquitous Challenge . Springer, 2005, pp. 682–689
2005
Earlier work this paper cites.
P. D'haeseleer, “How does gene expression clustering work?” Nature Biotechnology , vol. 23, no. 12, pp. 1499–1501, Dec. 2005
2005
Earlier work this paper cites.
P. Geurts, N. Touleimat, M. Dutreix, and F. d’Alché-Buc, “Inferring biological networks with output kernel trees,” BMC Bioinform. , vol. 8, no. S-2, 2007
2007
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
2007
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results,” http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html
2007
Earlier work this paper cites.
C. D. Manning, P. Raghavan, and H. Schütze, Introduction to information retrieval . Cambridge University Press, 2008
2008
Cited alongside, same era.
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning . Springer New York, 2009
2009
Cited alongside, same era.
A. K. Jain, “Data clustering: 50 years beyond k-means,” Pattern Recognition Letters , vol. 31, no. 8, pp. 651–666, 2010
2010
Cited alongside, same era.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller, “How to explain individual classification decisions,” Journal of Machine Learning Research , vol. 11, pp. 1803–1831, 2010
2010
Cited alongside, same era.
D. Sculley, “Web-scale k-means clustering,” in WWW . ACM, 2010, pp. 1177–1178
2010
Cited alongside, same era.
K. G. Dizaji, A. Herandi, C. Deng, W. Cai, and H. Huang, “Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization,” in ICCV . IEEE Computer Society, 2017, pp. 5747–5756
2017
Later among the works it cites.
X. Guo, X. Liu, E. Zhu, and J. Yin, “Deep clustering with convolutional autoencoders,” in ICONIP (2) , ser. Lecture Notes in Computer Science, vol. 10635. Springer, 2017, pp. 373–382
2017
Later among the works it cites.
M. Kern, A. Lex, N. Gehlenborg, and C. R. Johnson, “Interactive visual exploration and refinement of cluster assignments,” BMC Bioinformatics , vol. 18, no. 1, Sep. 2017
2017
Later among the works it cites.
L. M. Zintgraf, T. S. Cohen, T. Adel, and M. Welling, “Visualizing deep neural network decisions: Prediction difference analysis,” in ICLR (Poster) . OpenReview.net, 2017
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
K. Hansen, D. Baehrens, T. Schroeter, M. Rupp, and K.-R. Müller, “Visual interpretation of kernel-based prediction models,” Molecular Informatics , vol. 30, no. 9, pp. 817–826, 2011
2011
Cited alongside, same era.
V. Satopaa, J. Albrecht, D. Irwin, and B. Raghavan, “Finding a ”kneedle” in a haystack: Detecting knee points in system behavior,” in 2011 31st International Conference on Distributed Computing Systems Workshops , 2011, pp. 166–171
2011
Cited alongside, same era.
A. Coates, A. Y. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” in AISTATS , ser. JMLR Proceedings, vol. 15. JMLR.org, 2011, pp. 215–223
2011
Cited alongside, same era.
G. Ciriello, M. L. Miller, B. A. Aksoy, Y. Senbabaoglu, N. Schultz, and C. Sander, “Emerging landscape of oncogenic signatures across human cancers,” Nature Genetics , vol. 45, no. 10, pp. 1127–1133, Sep. 2013
2013
Cited alongside, same era.
W. Landecker, M. D. Thomure, L. M. A. Bettencourt, M. Mitchell, G. T. Kenyon, and S. P. Brumby, “Interpreting individual classifications of hierarchical networks,” in IEEE Symposium on Computational Intelligence and Data Mining , 2013, pp. 32–38
2013
Cited alongside, same era.
R. Fraiman, B. Ghattas, and M. Svarc, “Interpretable clustering using unsupervised binary trees,” Adv. Data Anal. Classif. , vol. 7, no. 2, pp. 125–145, 2013
2013
Cited alongside, same era.
S. Lapuschkin, A. Binder, K.-R. Müller, and W. Samek, “Understanding and comparing deep neural networks for age and gender classification,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 1629–1638
2017
Later among the works it cites.
Y. Ding, Y. Liu, H. Luan, and M. Sun, “Visualizing and understanding neural machine translation,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics , 2017, pp. 1150–1159
2017
Later among the works it cites.
L. Arras, F. Horn, G. Montavon, K.-R. Müller, and W. Samek, ““What is relevant in a text document?”: An interpretable machine learning approach,” PLOS ONE , vol. 12, no. 8, p. e0181142, 2017
2017
Later among the works it cites.
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
2017
Later among the works it cites.
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba, “Network dissection: Quantifying interpretability of deep visual representations,” in CVPR . IEEE Computer Society, 2017, pp. 3319–3327
2017
Later among the works it cites.
M. Caron, P. Bojanowski, A. Joulin, and M. Douze, “Deep clustering for unsupervised learning of visual features,” in 15th European Conference on Computer Vision , 2018, pp. 139–156
2018
Later among the works it cites.
G. Montavon, W. Samek, and K.-R. Müller, “Methods for interpreting and understanding deep neural networks,” Digital Signal Processing , vol. 73, pp. 1–15, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
M. Meila, “How to tell when a clustering is (approximately) correct using convex relaxations,” in Advances in Neural Information Processing Systems 31 , 2018, pp. 7418–7429
2018
Later among the works it cites.
B. Zhou, D. Bau, A. Oliva, and A. Torralba, “Interpreting deep visual representations via network dissection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–1, 2018
2018
Later among the works it cites.
W. Samek, G. Montavon, A. Vedaldi, L. K. Hansen, and K.-R. Müller, Explainable AI: interpreting, explaining and visualizing deep learning . Springer Nature, 2019, vol. 11700
2019
Closest in time.
G. Montavon, A. Binder, S. Lapuschkin, W. Samek, and K.-R. Müller, “Layer-wise relevance propagation: An overview,” in Explainable AI , ser. Lecture Notes in Computer Science. Springer, 2019, vol. 11700, pp. 193–209
2019
Closest in time.
F. Horst, S. Lapuschkin, W. Samek, K.-R. Müller, and W. I. Schöllhorn, “Explaining the unique nature of individual gait patterns with deep learning,” Scientific Reports , vol. 9, p. 2391, Feb. 2019
2019
Closest in time.
L. Perotin, R. Serizel, E. Vincent, and A. Guérin, “CRNN-based multiple DoA estimation using acoustic intensity features for ambisonics recordings,” J. Sel. Topics Signal Processing , vol. 13, no. 1, pp. 22–33, 2019
2019
Closest in time.
G. Montavon, “Gradient-based vs. propagation-based explanations: An axiomatic comparison,” in Explainable AI , ser. Lecture Notes in Computer Science. Springer, 2019, vol. 11700, pp. 253–265
2019
Closest in time.
L. Arras, J. A. Arjona-Medina, M. Widrich, G. Montavon, M. Gillhofer, K.-R. Müller, S. Hochreiter, and W. Samek, “Explaining and interpreting LSTMs,” in Explainable AI , ser. Lecture Notes in Computer Science. Springer, 2019, vol. 11700, pp. 211–238
2019
Closest in time.
S. Lapuschkin, S. Wäldchen, A. Binder, G. Montavon, W. Samek, and K.-R. Müller, “Unmasking Clever Hans predictors and assessing what machines really learn,” Nature Communications , vol. 10, p. 1096, 2019
2019
Closest in time.
W. V. Gansbeke, S. Vandenhende, S. Georgoulis, M. Proesmans, and L. V. Gool, “SCAN: learning to classify images without labels,” in ECCV (10) , ser. Lecture Notes in Computer Science, vol. 12355. Springer, 2020, pp. 268–285
2020
Closest in time.
J. Kauffmann, K.-R. Müller, and G. Montavon, “Towards explaining anomalies: A deep Taylor decomposition of one-class models,” Pattern Recognit. , vol. 101, p. 107198, 2020
2020
Closest in time.
M. Moshkovitz, S. Dasgupta, C. Rashtchian, and N. Frost, “Explainable k-means and k-medians clustering,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event , ser. Proceedings of Machine Learning Research, vol. 119. PMLR, 2020, pp. 7055–7065
2020
Closest in time.
J. O. Hanson, J. R. Rhodes, S. H. M. Butchart, G. M. Buchanan, C. Rondinini, G. F. Ficetola, and R. A. Fuller, “Global conservation of species’ niches,” Nature , vol. 580, no. 7802, pp. 232–234, Mar. 2020
2020
Closest in time.
O. Eberle, J. Büttner, F. Krautli, K.-R. Müller, M. Valleriani, and G. Montavon, “Building and interpreting deep similarity models,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2020
2020
Closest in time.
2020
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.
T. Schnake, O. Eberle, J. Lederer, S. Nakajima, K. T. Schütt, K.-R. Müller, and G. Montavon, “Higher-order explanations of graph neural networks via relevant walks,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
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.