Weight Interpretability

Parameter Interpretability

Weights are a vector in parameter space. Attribution is an effect of weight and feature is an effect of representation. The motivation for the weight-similarity is to avoid components sharing param.

SVD cannot treat
Superposition Hypothesis

NMF also limited to
Superposition Hypothesis

Weight Interpretability Notion

Weight Superposition

Super Weight

Weight Changing

Weight Interpretability Methods

APD

Meta SAE

Stochastic Parameter Decomposition

MathNeuro

Bilinear MLPs

arxiv.org

https://arxiv.org/pdf/2410.08417

Achille and Soatto (2018) studied the amount of information stored in the weights of deep networks

Emergence of Invariance and Disentanglement in Deep Representations

Using established principles from Statistics and Information Theory, we show that invariance to nuisance factors in a deep neural network is equivalent to information minimality of the learned...

https://arxiv.org/abs/1706.01350

There is little superposition in parameter space. Linearity in parameter space is a reasonable assumption.

arxiv.org

https://arxiv.org/pdf/2505.15811

arxiv.org

https://arxiv.org/pdf/1804.08838

Weight Interpretability

Parameter Interpretability

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