Introduction:

'How can we control whether superposition and polysemanticity occur?’ was one of the open questions mentioned by the authors of ‘Toy Models of Superposition’ and this work was an attempt to do so with the help of Active Learning.

The idea behind this work was to examine whether it is possible to decode Superposition using Active Learning methods. While it seems that Superposition is an attempt to arrange more features in a smaller space to utilize the limited resources better, it might be worth inspecting if Superposition is dependent on any other factors. I have compared the presence of Superposition in ResNet18’s baseline and Active Learning model trained on CIFAR10 and TinyImageNet dataset. The presence of Superposition in the above models is inspected across multiple criteria, including t-SNE visualizations, cosine similarity histograms, Silhouette Scores, and Davies-Bouldin Indexes. Contrary to my expectations, the active learning model did not significantly outperform the baseline in terms of feature separation and overall accuracy. This suggests that non-informative sample selection and potential overfitting to uncertain samples may have hindered the active learning model's ability to generalize better suggesting more sophisticated approaches might be needed to decode superposition and potentially reduce it.

I wanted to test whether the hypothesis in Figure 1 is plausible, it felt intuitive to imagine the superposition being reduced if the model has been able to ‘focus’ more on difficult features, allowing it to give less attention to the redundant features.
Thus reducing the overall required feature representation and helping in observing lesser superposition.

Superposition:

For most, when one hears the term, Superposition it is immediately clubbed with quantum mechanics, the very intriguing quantum entanglement, and always reminds us of the ‘thought experiment’ by Erwin Schrödinger in 1935. But the circuits thread introduced this word Superposition in a new light, a new meaning for anyone interested in the field of mechanistic interpretability. Just as Schrödinger’s cat can be both alive and dead at the same time in his thought experiment, it was observed how a neuron can activate for both a car wheel and a dog snoot. The authors of Zoom In: An Introduction to Circuits observed that neurons had conn...

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