Understand
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.
Built on
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