  Neural network models often struggle with high-dimensional but small
sample-size tabular datasets. One reason is that current weight initialisation
methods assume independence between weights, which can be problematic when
there are insufficient samples to estimate the model's parameters accurately.
In such small data scenarios, leveraging additional structures can improve the
model's performance and training stability. To address this, we propose
GCondNet, a general approach to enhance neural networks by leveraging implicit
structures present in tabular data. We create a graph between samples for each
data dimension, and utilise Graph Neural Networks (GNNs) for extracting this
implicit structure, and for conditioning the parameters of the first layer of
an underlying predictor network. By creating many small graphs, GCondNet
exploits the data's high-dimensionality, and thus improves the performance of
an underlying predictor network. We demonstrate the effectiveness of our method
on 9 real-world datasets, where GCondNet outperforms 15 standard and
state-of-the-art methods. The results show that GCondNet is a versatile
framework for injecting graph-regularisation into various types of neural
networks, including MLPs and tabular Transformers.
