Euclidean-equivariant functions on three-dimensional point clouds
Abstract/Contents
- Abstract
- We present a type of neural network that is locally equivariant to 3D rotations, translations, and permutations of points at every layer. Local 3D rotation equivariance removes the need for data augmentation to identify features in arbitrary orientations. These networks use convolution filters built from spherical harmonics; due to the mathematical consequences of this filter choice, each layer accepts as input (and guarantees as output) scalars, vectors, and higher-order tensors, in the geometric sense of these terms. We exhibit applications of these networks to supervised learning tasks in structural biology.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Thomas, Nathaniel Cabot |
|---|---|
| Degree supervisor | Hayden, Patrick (Patrick M.) |
| Thesis advisor | Hayden, Patrick (Patrick M.) |
| Thesis advisor | Dror, Ron, 1975- |
| Thesis advisor | Ganguli, Surya, 1977- |
| Degree committee member | Dror, Ron, 1975- |
| Degree committee member | Ganguli, Surya, 1977- |
| Associated with | Stanford University, Department of Physics. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Nathaniel Cabot Thomas. |
|---|---|
| Note | Submitted to the Department of Physics. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Nathaniel Cabot Thomas
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