Instead of learning only from a model's input examples or output predictions, this approach examines collections of model weights. A learned representation can summarize information useful for comparing models or predicting characteristics.
Parameter structure matters: networks can represent similar behavior with differently arranged weights. Architecture differences and reconstruction errors complicate reuse, and generated weights still require behavioral evaluation.
ELI5
Weight space learning studies the learned numbers inside other neural networks. It tries to find useful patterns across those collections of numbers.
For example, a system could study many trained classifiers and predict which are likely to work well on a test. That prediction still needs to be checked.
Is it the same as ordinary model training?
No. Its input domain includes parameters of other models, rather than only examples such as text or images.
Does generating weights guarantee a useful model?
No. The resulting network must be tested, and it may need further training before it performs the intended task.
