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FANS 2021: 18th Annual Conference Foundations of Conscience Monday 12 April 2021 (Invited) Super resolution with and without deep learning J Bad, B Leland, M Leek, A Bristol, X Hào, W Buying, C Zimmer Institute
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How to fill out rotation invariance in graph

01
Start by defining the graph that you want to make rotation invariant.
02
Identify the features of the graph that need to be invariant to rotations.
03
Use techniques such as graph convolutional networks or graph isomorphism networks to learn rotation-invariant representations.
04
Apply data augmentation techniques such as rotating the graph and using different orientations to improve the model's rotation invariance.
05
Test the model's performance on rotated versions of the graph to ensure that it is indeed rotation invariant.

Who needs rotation invariance in graph?

01
Researchers and practitioners working on tasks involving graph data, such as molecular structure analysis, social network analysis, and image recognition, may benefit from rotation invariance in graphs.
02
Applications requiring consistent features regardless of orientation or orientation changes can also benefit from rotation invariance in graphs.
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Rotation invariance in graphs refers to the property that certain graph characteristics or features remain unchanged under any rotation of the graph. This means that the structural properties of the graph do not depend on the orientation of its vertices and edges.
Entities that utilize rotationally invariant models in their graph analyses, such as researchers or organizations in fields like computer science, physics, or data science, may be required to file information regarding rotation invariance in graph, depending on regulatory or governance standards.
Filling out rotation invariance in a graph typically involves documenting the methods used to establish its invariance, the properties analyzed, and any relevant graph metrics. This can include algorithms or formulas that demonstrate the invariance properties.
The purpose of rotation invariance in graphs is to ensure that analyses and applications based on graph structures are robust to changes in perspective, thereby enabling reliable comparisons and generalizations across different orientations.
The information that must be reported typically includes the mathematical definitions of rotation invariance, any theorems or proofs supporting the invariance, experimental results showcasing the invariance, and the methodologies employed to achieve and analyze it.
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