A parameter-neutral fuzzy-logic FFN augmented with self-forgetting quantifiers produces legible grammatical-licensing detectors while matching baseline perplexity on OpenWebText.
Advances in Neural Information Processing Systems (NeurIPS) , year=
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Sparse autoencoder analysis of PatchTST FFN activations shows sparse, stable representations with no empirical support for superposition on standard time series forecasting tasks.
citing papers explorer
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Explicit Fuzzy Logic in the Feed-Forward Layer: Self-Forgetting Quantifiers Discover Legible Grammatical-Licensing Detectors
A parameter-neutral fuzzy-logic FFN augmented with self-forgetting quantifiers produces legible grammatical-licensing detectors while matching baseline perplexity on OpenWebText.
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Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting
Sparse autoencoder analysis of PatchTST FFN activations shows sparse, stable representations with no empirical support for superposition on standard time series forecasting tasks.