3D steerable CNNs: Learning rotationally equivariant features in volumetric data

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

3D steerable CNNs : Learning rotationally equivariant features in volumetric data. / Weiler, Maurice; Geiger, Mario; Welling, Max; Boomsma, Wouter; Cohen, Taco.

Proceedings of the 32nd International Conference on Neural Information Processing Systems. Bind 2018 derc. udg. NIPS Proceedings, 2018. s. 10381-10392 (Advances in Neural Information Processing Systems, Bind 31).

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Weiler, M, Geiger, M, Welling, M, Boomsma, W & Cohen, T 2018, 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. i Proceedings of the 32nd International Conference on Neural Information Processing Systems. derc udg, bind 2018, NIPS Proceedings, Advances in Neural Information Processing Systems, bind 31, s. 10381-10392, 32nd Annual Conference on Neural Information Processing Systems, Montreal, Canada, 02/12/2018.

APA

Weiler, M., Geiger, M., Welling, M., Boomsma, W., & Cohen, T. (2018). 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. I Proceedings of the 32nd International Conference on Neural Information Processing Systems (derc udg., Bind 2018, s. 10381-10392). NIPS Proceedings. Advances in Neural Information Processing Systems Bind 31

Vancouver

Weiler M, Geiger M, Welling M, Boomsma W, Cohen T. 3D steerable CNNs: Learning rotationally equivariant features in volumetric data. I Proceedings of the 32nd International Conference on Neural Information Processing Systems. derc udg. Bind 2018. NIPS Proceedings. 2018. s. 10381-10392. (Advances in Neural Information Processing Systems, Bind 31).

Author

Weiler, Maurice ; Geiger, Mario ; Welling, Max ; Boomsma, Wouter ; Cohen, Taco. / 3D steerable CNNs : Learning rotationally equivariant features in volumetric data. Proceedings of the 32nd International Conference on Neural Information Processing Systems. Bind 2018 derc. udg. NIPS Proceedings, 2018. s. 10381-10392 (Advances in Neural Information Processing Systems, Bind 31).

Bibtex

@inproceedings{38dcfaf2b371412cb00e4d441f8b8544,
title = "3D steerable CNNs: Learning rotationally equivariant features in volumetric data",
abstract = "We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.",
author = "Maurice Weiler and Mario Geiger and Max Welling and Wouter Boomsma and Taco Cohen",
year = "2018",
language = "English",
volume = "2018",
series = "Advances in Neural Information Processing Systems",
publisher = "NIPS Proceedings",
pages = "10381--10392",
booktitle = "Proceedings of the 32nd International Conference on Neural Information Processing Systems",
edition = "derc",
note = "32nd Annual Conference on Neural Information Processing Systems, NeurIPS ; Conference date: 02-12-2018 Through 08-12-2018",
url = "https://nips.cc/Conferences/2018",

}

RIS

TY - GEN

T1 - 3D steerable CNNs

T2 - 32nd Annual Conference on Neural Information Processing Systems

AU - Weiler, Maurice

AU - Geiger, Mario

AU - Welling, Max

AU - Boomsma, Wouter

AU - Cohen, Taco

N1 - Conference code: 32

PY - 2018

Y1 - 2018

N2 - We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.

AB - We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry.

UR - http://www.mendeley.com/research/3d-steerable-cnns-learning-rotationally-equivariant-features-volumetric-data

UR - http://www.mendeley.com/research/3d-steerable-cnns-learning-rotationally-equivariant-features-volumetric-data

M3 - Article in proceedings

VL - 2018

T3 - Advances in Neural Information Processing Systems

SP - 10381

EP - 10392

BT - Proceedings of the 32nd International Conference on Neural Information Processing Systems

PB - NIPS Proceedings

Y2 - 2 December 2018 through 8 December 2018

ER -

ID: 236511653