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6th Int. Conf. Learn. Representations (ICLR) , 2018 .
[65] N. Lang , K. Schindler , and J. D. Wegner , “Country-wide high-
resolution vegetation height mapping with Sentinel-2 ,” Remote
Sens. Environ. , vol. 233, p. 111,347 , Nov. 2019 . doi: 10.1016/j.
rse.2019.111347 .
[66] U. Kälin , N. Lang, C. Hug, A. Gessler , and J. D. Wegner , “Defoliation
estimation of forest trees from ground-level images ,” Remote Sens. En -
viron. , vol. 223, pp. 143–153, Mar. 2019. doi: 10.1016/j.rse.2018.12.021 .
[67] M. Raissi , “Deep hidden models: Deep learning of nonlinear
partial differential equations ,” J. Mach. Learn. Res. , vol. 19,
pp. 1–24, Jan. 2018 . doi: 10.5555/3291125.3291150 .
[68] D. Svendsen , M. Piles , J. Muñoz-Marí , D. Luengo , L. Martino ,
and G. Camps-Valls , “Integrating domain knowledge in data-
driven earth observation with process convolutions ,” submitted
for publication.
[69] L. Von Rueden , S. Mayer , J. Garcke , C. Bauckhage , and J. Schueck -
er, “Informed machine learning–towards a taxonomy of explicit
integration of knowledge into machine learning ,” Learning , vol.
18, pp. 19–20, Mar. 2019 .
[70] E. de Bézenac , A. Pajot , and P. Gallinari , “Deep learning for
physical processes: Incorporating prior scientific knowledge ,” J.
Statist. Mech., Theory Exp. , vol. 2019 , no. 12, p. 124,009 , 2019 . doi:
10.1088/1742-5468/ab3195 .
[71] R. R. Selvaraju , M. Cogswell , A. Das, R. Vedantam , D. Parikh ,
and D. Batra , “Grad-cam: Visual explanations from deep net -
works via gradient-based localization ,” in Proc. IEEE Int. Conf.
Comput. Vis. (ICCV) , Oct. 2017 , pp. 618–626.
[72] M. M . Losch , M. Fritz, and B. Schiele , “Interpretability beyond
classification output: Semantic bottleneck networks ,” 2019 . [On -
line]. Available: http://arxiv.org/abs/1907.10882
[73] D. Huk Park et al., “Multimodal explanations: Justifying deci -
sions and pointing to the evidence ,” in Proc. IEEE Conf. Comput.
Vis. Pattern Recognit. (CVPR) , June 2018 , pp. 1–10.
[74] J. Adebayo , J. Gilmer , M. Muelly , I. J. Goodfellow , M. Hardt , and
B. Kim, “Sanity checks for saliency maps ,” in Proc. Neural Inf. Pro -
cess. Syst. , 2018 .
[75] T. Ye, X. Wang , J. Davidson , and A. Gupta , “Interpretable in -
tuitive physics model ,” in Proc. Eur. Conf. Comput. Vis. (ECCV) ,
2018 , pp. 87–102.
[76] D. Marcos , S. Lobry , and D. Tuia, “Semantically interpre -
table activation maps: What-where-how explanations within CNNs ,” in Proc. Int. Conf. Comput. Vision Workshop , 2019 ,
pp. 4207 –4215 .
[77] D. Marcos , S. Lobry , R. Fong , N. Courty , R. Flamary , and D. Tuia,
“Contextual semantic interpretability ,” in Proc. Asian Conf. Com -
put. Vis. (ACCV) , 2020 .
[78] S. Lapuschkin , S. Wäldchen , A. Binder , G. Montavon , W. Samek ,
and K.-R. Müller , “Unmasking Clever Hans predictors and as -
sessing what machines really learn ,” Nature Commun. , vol. 10,
no. 1, p. 1096, 2019 . doi: 10.1038/s41467-019-08987-4 .
[79] C. Rudin , “Stop explaining black box machine learning models
for high stakes decisions and use interpretable models instead ,”
Nature Mach. Intell. , vol. 1, no. 5, pp. 206–215, 2019 . doi: 10.1038/
s42256-019-0048-x .
[80] R. Iten, T. Metger , H. Wilming , L. del Rio , and R. Renner , “Dis-
covering physical concepts with neural networks ,” Phys. Rev.
Lett., vol. 124, no. 1, p. 010508, Jan. 2020 . doi: 10.1103/PhysRev -
Lett.124.010508 .
[81] K. Zhang , B. Schölkopf , P. Spirtes , and C. Glymour , “Learning cau -
sality and causality-related learning: Some recent progress ,” Nat.
Sci. Rev. , vol. 5, no. 1, pp. 26–29, 2018 . doi: 10.1093/nsr/nwx137 .
[82] J. Runge et al., “Inferring causation from time series with per -
spectives in Earth system sciences ,” Nature Commun. , vol. 10, no.
1, p. 2553 , 2019 . doi: 10.1038/s41467-019-10105-3 .
[83] J. Pearl , Causality: Models, Reasoning and Inference , 2nd ed . New
York: Cambridge Univ. Press , 2009 .
[84] C. Granger , “Investigating causal relations by econometric mod -
els and cross-spectral methods ,” Econometrica , vol. 37, no. 3, pp.
424–438, 1969 . doi: 10.2307/1912791 .
[85] D. Marinazzo , M. Pellicoro , and S. Stramaglia , “Kernel method
for nonlinear granger causality ,” Phys. Rev. Lett. , vol. 100, no. 14,
p. 144,103, Apr. 2008 . doi: 10.1103/PhysRevLett.100.144103 .
[86] D. Bueso , M. Piles , and, and G. Camps-Valls , “Cross-information
kernel causality: Revisiting global teleconnections of ENSO over
soil moisture and vegetation ,” in Proc. Climate Informatics , Paris,
France, Oct. 2 –4, 2019 , pp. 1–5.
[87] G. Sugihara et al., “Detecting causality in complex ecosystems ,”
Science , vol. 338, no. 6106 , pp. 496–500, 2012 . doi: 10.1126/
science.1227079 .
[88] G. Camps-Valls et al., “Inferring causal graphs from observation -
al long-term carbon and water fluxes records ,” presented at the
AGU Fall Meeting , San Francisco, Dec. 9 –13, 2019 .
[89] Y. Bengio et al., “A meta-transfer objective for learning to disen -
tangle causal mechanisms ,” 2019 , arXiv:1901.10912.
[90] C. Louizos , U. Shalit , J. M. Mooij , D. Sontag , R. Zemel , and M.
Welling , “Causal effect inference with deep latent-variable mod -
els,” in Proc. Adv. Neural Inf. Process. Syst. 30 , I. Guyon , U. V. Lux-
burg, S. Bengio , H. Wallach , R. Fergus , S. Vishwanathan , and R.
Garnett , Eds. Curran Associates , 2017, pp. 6446 –6456 . [Online].
Available: http://papers.nips.cc/paper/7223-causal-effect-infer -
ence-with-deep-latent-variable-models.pdf
[91] D. Bueso , M. Piles , and G. Camps-Valls , “Nonlinear PCA for spa -
tio-temporal analysis of earth observation data ,” IEEE Trans. Geos -
ci. Remote Sens. , vol. 58, no. 8, pp. 5752 –5763 , 2019 . doi: 10.1109/
TGRS.2020.2969813 .
GRS
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Segmenting across places: The need for fair transfer learning with
satellite imagery
Miao Zhang Harvineet Singh Lazarus Chok Rumi Chunara
New York University