Interpretability for Turing Machines
Read the original on arXiv Machine Learning →The paper demonstrates that the interpretability method known as susceptibilities, originally used for neural networks, can detect algorithmic structure in Turing machines by examining the local loss landscape of a learning problem for noisy Turing machines. It proves that symmetries and path separation in a Turing machine’s algorithm produce permutation symmetries and low‑rank blocks in the susceptibility matrix. Empirical studies on deterministic finite automata show that algorithmic features can be recovered through principal component analysis and clustering in susceptibility space.
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