Abstract
We introduce a solid harmonic wavelet scattering representation, invariant to rigid motion and stable to deformations, for regression and classification of 2D and 3D signals. Solid harmonic wavelets are computed by multiplying solid harmonic functions with Gaussian windows dilated at different scales. Invariant scattering coefficients are obtained by cascading such wavelet transforms with the complex modulus nonlinearity. We study an application of solid harmonic scattering invariants to the estimation of quantum molecular energies, which are also invariant to rigid motion and stable with respect to deformations. A multilinear regression over scattering invariants provides close to state of the art results over small and large databases of organic molecules.
| Original language | English |
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| Pages (from-to) | 6541-6550 |
| Number of pages | 10 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2017-December |
| Publication status | Published - 31 Dec 2017 |
| Event | 31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, USA United States Duration: 4 Dec 2017 → 9 Dec 2017 |
Bibliographical note
Funding Information:M.E., G.E. and S.M. are supported by ERC grant InvariantClass 320959; M.H. is supported by the Alfred P. Sloan Fellowship, the DARPA YFA, and NSF grant 1620216.
Publisher Copyright:
© 2017 Neural information processing systems foundation. All rights reserved.
Funding
M.E., G.E. and S.M. are supported by ERC grant InvariantClass 320959; M.H. is supported by the Alfred P. Sloan Fellowship, the DARPA YFA, and NSF grant 1620216.
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing