get_input_inclusions

Useful when the number of inputs and/or hidden neurons are very
large, and direct visualization of the network is difficult.

internal

Latent binary Bayesian neural networks (LBBNNs) are implemented using
'torch', an R interface to the LibTorch backend. Supports mean-field variational
inference as well as flexible variational posteriors using normalizing flows.
The standard LBBNN implementation follows Hubin and Storvik (2024) <doi:10.3390/math12060788>,
using the local reparametrization trick as in Skaaret-Lund et al. (2024)
<https://openreview.net/pdf?id=d6kqUKzG3V>. Input-skip connections are also supported,
as described in Høyheim et al. (2025) <doi:10.48550/arXiv.2503.10496>.

Lars Skaaret-Lund

LBBNN

Latent Binary Bayesian Neural Networks Using 'torch'

Aliaksandr Hubin

Eirik Høyheim

get_input_inclusions function

<dl><dt>model</dt>
<dd>An instance of <code>LBBNN_Net</code> where <code>input_skip = TRUE</code>.</dd></dl>

Arguments

Function that checks how many times inputs are included, and from which layer. Used in summary function. — get_input_inclusions

<dl>

<dt>model</dt>
<dd>An instance of <code>LBBNN_Net</code> where <code>input_skip = TRUE</code>.</dd>

</dl>

get_input_inclusions: Function that checks how many times inputs are included, and from which layer. Used in summary function.

Description

Usage

Value

Arguments