A function machine is a generalization of neural networks to potentially infinite dimensional layers, motivated by the study of universal approximation of operators and functionals over abstract Banach spaces.

Definition

A function machine’s layers is defined by the type of layers it is composed with.

Definition

Let $K \subset \mathbb{R}^d, K' \subset \mathbb{R}^{d'}$ both compact, $T$ is some affine map.

(Operator layer) When $T:\mathcal{L}^1(K, \mu)\to \mathcal{L}^1(K', \mu)$, $T = T^\mathfrak{o}$ is said to be an operator layer if there is some continuous family of measures $(W_t \ll \mu)$ indexed by $t \in K'$ and a function $b \in \mathcal{L}^1(K, \mu)$ such that:

$T^\mathfrak{o}: f \mapsto \left(t \mapsto \int_K f dW_t + b(t)\right)$

(Functional layer) When $T:\mathcal{L}^1(K, \mu) \to \mathbb{R}^{d'}$, $T = T^\mathfrak{f}$ is said to be a functional layer if there is some measure $W \ll \mu$ and a vector $b \in \mathbb{R}^d$ such that:

$T^\mathfrak{f}: f \mapsto \int f dW + b$

(Basis layer) When $T:\mathbb{R}^d \to \mathcal{L}^1(K',\mu)$, $T=T^\mathfrak{b}$ is said to be a basis layer if there is some function $w:K'\to\mathbb{R}^d$ and $b \in \mathcal{L}^1(K', \mu)$ such that:

(Fully-connected layer) When the inputs and outputs of a layer are finite dimensional, we yield the standard fully-connected layer, denoted $T^\mathfrak{n}$

One can now, for instance, define an 1-hidden layer operator network to be the composition of two operator layers:

$F[f] = (T^\mathfrak{o}\circ g \circ T^\mathfrak{o})[f] = s \mapsto b'(s) + \int_{K''}g\left(\int_K f dW_t + b(t) \right)dW_s$

where $(W_t \ll \mu)_{t \in K''}$, $(W_s\ll\mu)_{s \in K'}$ are families of absolutely continuous measures w.r.t. the Lebesgue measure $\mu$, $b$ and $b'$ are measurable bias functions over compact domains $K''$ and $K'$ respectively, and $g$ an activation function.

William Guss, Deep Function Machines: Generalized Neural Networks for Topological Layer Expression,(arXiv:1612.04799

William Guss, Ruslan Salakhutdinov, On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps,(arXiv:1910.01545

Last revised on January 14, 2020 at 22:15:44.
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