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We present a novel approach to the regression of quantum mechanical energies based on a scattering transform of an intermediate electron density representation.
Inhomogeneous electron gas
Hohenberg, P. and Kohn, W · 1964
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Orthogonal least squares learning algorithm for radial basis function networks
Chen, S., Cowan, C.F.N., and Grant, P.M · 1991
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Toward reliable density functional methods without adjustable parameters: The pbe0 model
Adamo, C. and Barone, V · 1999
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Wavelets, ridgelets and curvelets on the sphere
Starck, J.-L, Moudden, Y., Abrial, P., and Nguyen, M · 2006
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On the difference between orthogonal matching pursuit and orthogonal least squares, 2007
Blumensath, T. and Davies, M.E · 2007
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A.P., Payne, M.C., Kondor, R., and Csányi, G · 2010
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G.E · 2012
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Group invariant scattering
Mallat, S · 2012
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Fast and accurate modeling of molecular atomization energies with machine learning
Rupp, M., Tkatchenko, A., Müller, K.R., and von Lilienfeld, O.A · 2012
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On representing chemical environments
Bartók, A.P., Kondor, R., and Csányi, G · 2013
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Invariant scattering convolution networks
Bruna, J. and Mallat, S · 2013
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Assessment and validation of machine learning methods for predicting molecular atomization energies
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O.A., Tkatchenko, A., and Müller, K.R · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y · 2013
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Rotation, scaling and deformation invariant scattering for texture discrimination
Sifre, L. and Mallat, S · 2013
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Deep roto-translation scattering for object classification
Oyallon, E. and Mallat, S · 2014
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