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Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena.
S. Chatterjee, S. Chen, and A. Banerjee, “Generalized dantzig selector: Application to the k-support norm,” in Advances in Neural Information Processing Systems , 2014, pp. 1934–1942
1942
Earlier work this paper cites.
P. Hohenberg and W. Kohn, “Inhomogeneous electron gas,” Physical review , vol. 136, no. 3B, p. B864, 1964
1964
Earlier work this paper cites.
K. Beven and A. Binley, “The future of distributed models: model calibration and uncertainty prediction,” Hydrological processes , vol. 6, no. 3, pp. 279–298, 1992
1992
Earlier work this paper cites.
R. Agrawal, “The continuum-armed bandit problem,” SIAM journal on control and optimization , vol. 33, no. 6, pp. 1926–1951, 1995
1995
Earlier work this paper cites.
V. N. Vapnik and V. Vapnik, Statistical learning theory . Wiley New York, 1998, vol. 1
1998
Earlier work this paper cites.
R. L. Wilby, T. Wigley, D. Conway, P. Jones, B. Hewitson, J. Main, and D. Wilks, “Statistical downscaling of general circulation model output: a comparison of methods,” Water resources research , vol. 34, no. 11, pp. 2995–3008, 1998
1998
Earlier work this paper cites.
P. Baldi and S. Brunak, Bioinformatics: the machine learning approach . MIT press, 2001
2001
Earlier work this paper cites.
J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning . Springer series in statistics Springer, Berlin, 2001, vol. 1
2001
Earlier work this paper cites.
J. Pei and J. Han, “Constrained frequent pattern mining: a pattern-growth view,” ACM SIGKDD Explorations Newsletter , vol. 4, no. 1, pp. 31–39, 2002
2002
Earlier work this paper cites.
R. D. Kleinberg, “Nearly tight bounds for the continuum-armed bandit problem,” in Advances in Neural Information Processing Systems , 2004, pp. 697–704
2004
Earlier work this paper cites.
B. P. Roe, H.-J. Yang, J. Zhu, Y. Liu, I. Stancu, and G. McGregor, “Boosted decision trees as an alternative to artificial neural networks for particle identification,” Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , vol. 543, no. 2, pp. 577–584, 2005
2005
Earlier work this paper cites.
P.-N. Tan, M. Steinbach, and V. Kumar, Intorduction to Data Mining . Addison-Wesley, 2005
2005
Earlier work this paper cites.
M. D. Twa, S. Parthasarathy, C. Roberts, A. M. Mahmoud, T. W. Raasch, and M. A. Bullimore, “Automated decision tree classification of corneal shape,” Optometry and vision science: official publication of the American Academy of Optometry , vol. 82, no. 12, p. 1038, 2005
2005
Earlier work this paper cites.
J.-F. Boulicaut and B. Jeudy, “Constraint-based data mining,” in Data Mining and Knowledge Discovery Handbook . Springer, 2005, pp. 399–416
2005
Earlier work this paper cites.
C. C. Fischer, K. J. Tibbetts, D. Morgan, and G. Ceder, “Predicting crystal structure by merging data mining with quantum mechanics,” Nature materials , vol. 5, no. 8, pp. 641–646, 2006
2006
Earlier work this paper cites.
M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 68, no. 1, pp. 49–67, 2006
2006
Earlier work this paper cites.
Y. Gil, E. Deelman, M. Ellisman, T. Fahringer, G. Fox, D. Gannon, C. Goble, M. Livny, L. Moreau, and J. Myers, “Examining the challenges of scientific workflows,” Ieee computer , vol. 40, no. 12, pp. 26–34, 2007
2007
Earlier work this paper cites.
D. Graham-Rowe, D. Goldston, C. Doctorow, M. Waldrop, C. Lynch, F. Frankel, R. Reid, S. Nelson, D. Howe, S. Rhee et al. , “Big data: science in the petabyte era,” Nature , vol. 455, no. 7209, pp. 8–9, 2008
2008
Earlier work this paper cites.
C. Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired Magazine , 2008
2008
Earlier work this paper cites.
J. Friedman, T. Hastie, and R. Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,” Biostatistics , vol. 9, no. 3, pp. 432–441, 2008
2008
Earlier work this paper cites.
S. Basu, I. Davidson, and K. Wagstaff, Constrained clustering: Advances in algorithms, theory, and applications . CRC Press, 2008
2008
Earlier work this paper cites.
G. Bell, T. Hey, and A. Szalay, “Beyond the data deluge,” Science , vol. 323, no. 5919, pp. 1297–1298, 2009
2009
Earlier work this paper cites.
A. Halevy, P. Norvig, and F. Pereira, “The unreasonable effectiveness of data,” Intelligent Systems, IEEE , vol. 24, no. 2, pp. 8–12, 2009
2009
Earlier work this paper cites.
J. Ginsberg, M. H. Mohebbi, R. S. Patel, L. Brammer, M. S. Smolinski, and L. Brilliant, “Detecting influenza epidemics using search engine query data,” Nature , vol. 457, no. 7232, pp. 1012–1014, 2009
2009
Earlier work this paper cites.
K. C. Wong, L. Wang, and P. Shi, “Active model with orthotropic hyperelastic material for cardiac image analysis,” in Functional Imaging and Modeling of the Heart . Springer, 2009, pp. 229–238
2009
Earlier work this paper cites.
G. M. James and P. Radchenko, “A generalized dantzig selector with shrinkage tuning,” Biometrika , vol. 96, no. 2, pp. 323–337, 2009
2009
Earlier work this paper cites.
L. Jacob, G. Obozinski, and J.-P. Vert, “Group lasso with overlap and graph lasso,” in Proceedings of the 26th annual international conference on machine learning . ACM, 2009, pp. 433–440
2009
Earlier work this paper cites.
G. Evensen, Data assimilation: the ensemble Kalman filter . Springer Science & Business Media, 2009
2009
Earlier work this paper cites.
T. Hey, S. Tansley, K. M. Tolle et al. , The fourth paradigm: data-intensive scientific discovery . Microsoft research Redmond, WA, 2009, vol. 1
2009
Earlier work this paper cites.
Economist, “The data deluge,” Special Supplement , 2010
2010
Cited alongside, same era.
G. Hautier, C. C. Fischer, A. Jain, T. Mueller, and G. Ceder, “Finding nature’s missing ternary oxide compounds using machine learning and density functional theory,” Chemistry of Materials , vol. 22, no. 12, pp. 3762–3767, 2010
2010
Cited alongside, same era.
T. Mikolov, M. Karafiát, L. Burget, J. Cernockỳ, and S. Khudanpur, “Recurrent neural network based language model.” in Interspeech , vol. 2, 2010, p. 3
2010
Cited alongside, same era.
S. Kim and E. P. Xing, “Tree-guided group lasso for multi-task regression with structured sparsity,” in Proceedings of the 27th International Conference on Machine Learning (ICML-10) , 2010, pp. 543–550
2010
Cited alongside, same era.
H. Denli, N. Subrahmanya et al. , “Multi-scale graphical models for spatio-temporal processes,” in Advances in Neural Information Processing Systems , 2014, pp. 316–324
2014
Later among the works it cites.
A. Karpatne, A. Khandelwal, S. Boriah, and V. Kumar, “Predictive learning in the presence of heterogeneity and limited training data.” in SDM . SIAM, 2014, pp. 253–261
2014
Later among the works it cites.
J. H. Faghmous, H. Nguyen, M. Le, and V. Kumar, “Spatio-temporal consistency as a means to identify unlabeled objects in a continuous data field,” in AAAI , 2014, pp. 410–416
2014
Later among the works it cites.
V. G. Honavar, “The promise and potential of big data: A case for discovery informatics,” Review of Policy Research , vol. 31, no. 4, pp. 326–330, 2014
2014
Later among the works it cites.
D. Castelvecchi et al. , “Artificial intelligence called in to tackle lhc data deluge,” Nature , vol. 528, no. 7580, pp. 18–19, 2015
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2010
Cited alongside, same era.
L. Li, W. Chu, J. Langford, and R. E. Schapire, “A contextual-bandit approach to personalized news article recommendation,” in Proceedings of the 19th international conference on World wide web . ACM, 2010, pp. 661–670
2010
Cited alongside, same era.
M. James, C. Michael, B. Brad, B. Jacques, D. Richard, R. Charles, and H. Angela, “Big data: The next frontier for innovation, competition, and productivity,” The McKinsey Global Institute , 2011
2011
Cited alongside, same era.
T. Jonathan, A. Gerald et al. , “Special issue: dealing with data,” Science , vol. 331, no. 6018, pp. 639–806, 2011
2011
Cited alongside, same era.
J. Kattge, S. Diaz, S. Lavorel, I. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. Wright et al. , “Try–a global database of plant traits,” Global change biology , vol. 17, no. 9, pp. 2905–2935, 2011
2011
Cited alongside, same era.
P. Melville and V. Sindhwani, “Recommender systems,” in Encyclopedia of machine learning . Springer, 2011, pp. 829–838
2011
Cited alongside, same era.
O. Chapelle and L. Li, “An empirical evaluation of thompson sampling,” in Advances in neural information processing systems , 2011, pp. 2249–2257
2011
Cited alongside, same era.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems , 2012, pp. 1097–1105
2012
Cited alongside, same era.
2015
Later among the works it cites.
J. H. Faghmous, V. Kumar, and S. Shekhar, “Computing and climate,” Computing in Science & Engineering , vol. 17, no. 6, pp. 6–8, 2015
2015
Later among the works it cites.
J. H. Faghmous, I. Frenger, Y. Yao, R. Warmka, A. Lindell, and V. Kumar, “A daily global mesoscale ocean eddy dataset from satellite altimetry,” Scientific data , vol. 2, 2015
2015
Later among the works it cites.
L. Li, J. C. Snyder, I. M. Pelaschier, J. Huang, U.-N. Niranjan, P. Duncan, M. Rupp, K.-R. Müller, and K. Burke, “Understanding machine-learned density functionals,” International Journal of Quantum Chemistry , 2015
2015
Later among the works it cites.
J. Xu, J. L. Sapp, A. R. Dehaghani, F. Gao, M. Horacek, and L. Wang, “Robust transmural electrophysiological imaging: Integrating sparse and dynamic physiological models into ecg-based inference,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015 . Springer, 2015, pp. 519–527
2015
Later among the works it cites.
A. Khandelwal, V. Mithal, and V. Kumar, “Post classification label refinement using implicit ordering constraint among data instances,” in Data Mining (ICDM), 2015 IEEE International Conference on . IEEE, 2015, pp. 799–804
2015
Later among the works it cites.
C. Paniconi and M. Putti, “Physically based modeling in catchment hydrology at 50: Survey and outlook,” Water Resources Research , vol. 51, no. 9, pp. 7090–7129, 2015
2015
Later among the works it cites.
M. F. Bierkens, “Global hydrology 2015: State, trends, and directions,” Water Resources Research , vol. 51, no. 7, pp. 4923–4947, 2015
2015
Later among the works it cites.
F. Schrodt, J. Kattge, H. Shan, F. Fazayeli, J. Joswig, A. Banerjee, M. Reichstein, G. Bönisch, S. Díaz, J. Dickie et al. , “Bhpmf–a hierarchical bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography,” Global Ecology and Biogeography , vol. 24, no. 12, pp. 1510–1521, 2015
2015
Later among the works it cites.
M. Jordan and T. Mitchell, “Machine learning: Trends, perspectives, and prospects,” Science , vol. 349, no. 6245, pp. 255–260, 2015
2015
Later among the works it cites.
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N. Wagner and J. M. Rondinelli, “Theory-guided machine learning in materials science,” Frontiers in Materials , vol. 3, p. 28, 2016
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Physics Informed Machine Learning Conference , Santa Fe, New Mexico, 2016
2016
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“Physical analytics, ibm research,” http://researcher.watson.ibm.com/researcher/view_group.php?id=6566
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2016
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2016
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