Learning on Model Weights using Tree Experts

The increasing availability of public models begs the question: can we train neural networks that use other networks as input? Such models allow us to study different aspects of a given neural network, for example, determining the categories in a model's training dataset. However, machine learning on model weights is challenging as they often exhibit significant variation unrelated to the models' semantic properties (nuisance variation). Here, we identify a key property of real-world models: most public models belong to a small set of Model Trees, where all models within a tree are fine-tuned from a common ancestor (e.g., a foundation model). Importantly, we find that within each tree there is less nuisance variation between models. Concretely, while learning across Model Trees requires complex architectures, even a linear classifier trained on a single model layer often works within trees. While effective, these linear classifiers are computationally expensive, especially when dealing with larger models that have many parameters. To address this, we introduce Probing Experts (ProbeX), a theoretically motivated and lightweight method. Notably, ProbeX is the first probing method specifically designed to learn from the weights of a single hidden model layer. We demonstrate the effectiveness of ProbeX by predicting the categories in a model's training dataset based only on its weights. Excitingly, ProbeX can also map the weights of Stable Diffusion into a shared weight-language embedding space, enabling zero-shot model classification.