  The effectiveness of training neural networks directly impacts computational
costs, resource allocation, and model development timelines in machine learning
applications. An optimizer's ability to train the model adequately (in terms of
trained model performance) depends on the model's initial weights. Model weight
initialization schemes use pseudorandom number generators (PRNGs) as a source
of randomness.
  We investigate whether substituting PRNGs for low-discrepancy quasirandom
number generators (QRNGs) -- namely Sobol' sequences -- as a source of
randomness for initializers can improve model performance. We examine
Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), Long
Short-Term Memory (LSTM), and Transformer architectures trained on MNIST,
CIFAR-10, and IMDB datasets using SGD and Adam optimizers. Our analysis uses
ten initialization schemes: Glorot, He, Lecun (both Uniform and Normal);
Orthogonal, Random Normal, Truncated Normal, and Random Uniform. Models with
weights set using PRNG- and QRNG-based initializers are compared pairwise for
each combination of dataset, architecture, optimizer, and initialization
scheme.
  Our findings indicate that QRNG-based neural network initializers either
reach a higher accuracy or achieve the same accuracy more quickly than
PRNG-based initializers in 60% of the 120 experiments conducted. Thus, using
QRNG-based initializers instead of PRNG-based initializers can speed up and
improve model training.
