Understand
Individual neurons in neural networks often represent a mixture of unrelated features.
- This phenomenon, called polysemanticity, can make interpreting neural networks more difficult and so we aim to understand its causes.
- We propose doing so through the lens of feature \emph{capacity}, which is the fractional dimension each feature consumes in the embedding space.
- We show that in a toy model the optimal capacity allocation tends to monosemantically represent the most important features, polysemantically represent less important features (in proportion to their impact on the loss), and entirely ignore the least important features.
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Feature visualization
Chris Olah, Alexander Mordvintsev, and Ludwig Schubert · 2017
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Zoom in: An introduction to circuits
Chris Olah, Nick Cammarata, Ludwig Schubert, Gabriel Goh, Michael Petrov, and Shan Carter · 2020
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