  Neural networks are powerful models that solve a variety of complex
real-world problems. However, the stochastic nature of training and large
number of parameters in a typical neural model makes them difficult to evaluate
via inspection. Research shows this opacity can hide latent undesirable
behavior, be it from poorly representative training data or via malicious
intent to subvert the behavior of the network, and that this behavior is
difficult to detect via traditional indirect evaluation criteria such as loss.
Therefore, it is time to explore direct ways to evaluate a trained neural model
via its structure and weights. In this paper we present MLDS, a new dataset
consisting of thousands of trained neural networks with carefully controlled
parameters and generated via a global volunteer-based distributed computing
platform. This dataset enables new insights into both model-to-model and
model-to-training-data relationships. We use this dataset to show clustering of
models in weight-space with identical training data and meaningful divergence
in weight-space with even a small change to the training data, suggesting that
weight-space analysis is a viable and effective alternative to loss for
evaluating neural networks.
