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arXiv · 2410.10811

Deep Linear Probe Generators for Weight Space Learning

Jonathan Kahana·Eliahu Horwitz·Imri Shuval·Yedid Hoshen
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Abstract

Weight space learning aims to extract information about a neural network, such as its training dataset or generalization error. Recent approaches learn directly from model weights, but this presents many challenges as weights are high-dimensional and include permutation symmetries between neurons. An alternative approach, Probing, represents a model by passing a set of learned inputs (probes) through the model, and training a predictor on top of the corresponding outputs. Although probing is typically not used as a stand alone approach, our preliminary experiment found that a vanilla probing baseline worked surprisingly well. However, we discover that current probe learning strategies are ineffective. We therefore propose Deep Linear Probe Generators (ProbeGen), a simple and effective modification to probing approaches. ProbeGen adds a shared generator module with a deep linear architecture, providing an inductive bias towards structured probes thus reducing overfitting. While simple, ProbeGen performs significantly better than the state-of-the-art and is very efficient, requiring between 30 to 1000 times fewer FLOPs than other top approaches.

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BibTeXRIS

Jonathan Kahana, Eliahu Horwitz, Imri Shuval, Yedid Hoshen. 2024-10-14. Deep Linear Probe Generators for Weight Space Learning. https://arxiv.org/abs/2410.10811

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