On Nonlinear Operators For Geometric Deep Learning
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**arXiv ID:** 2207.03485 **Authors:** Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon **Published:** 2022-07-06T06:45:33Z **Abstract:** This work studies operators mapping vector and scalar fields defined over a manifold $\mathcal{M}$, and which commute with its group of diffeomorphisms $\text{Diff}(\mathcal{M})$. We prove that in the case of scalar fields $L^p_ω(\mathcal{M,\mathbb{R}})$, those operators correspond to point-wise non-linearities, recovering and extending k...
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# On Non-Linear operators for Geometric Deep Learning
**arXiv ID:** 2207.03485
**Authors:** Grégoire Sergeant-Perthuis, Jakob Maier, Joan Bruna, Edouard Oyallon
**Published:** 2022-07-06T06:45:33Z
**Abstract:**
This work studies operators mapping vector and scalar fields defined over a manifold $\mathcal{M}$, and which commute with its group of diffeomorphisms $\text{Diff}(\mathcal{M})$. We prove that in the case of scalar fields $L^p_ω(\mathcal{M,\mathbb{R}})$, those operators correspond to point-wise non-linearities, recovering and extending known results on $\mathbb{R}^d$. In the context of Neural Networks defined over $\mathcal{M}$, it indicates that point-wise non-linear operators are the only universal family that commutes with any group of symmetries, and justifies their systematic use in combination with dedicated linear operators commuting with specific symmetries. In the case of vector fields $L^p_ω(\mathcal{M},T\mathcal{M})$, we show that those operators are solely the scalar multiplication. It indicates that $\text{Diff}(\mathcal{M})$ is too rich and that there is no universal class of non-linear operators to motivate the design of Neural Networks over the symmetries of $\mathcal{M}$.
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This skill is generated from the arXiv paper: On Non-Linear operators for Geometric Deep Learning (2207.03485).
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## References
- [arXiv:2207.03485](http://arxiv.org/abs/2207.03485v2)
