MapFormers learn cognitive maps via input-dependent Lie-algebra positional encodings and achieve near-perfect OOD generalization on cognitive tasks where standard transformers fail.
Hopfield networks is all you need, 2021
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Ordinary least squares is a special case of the single-layer linear transformer when attention parameters are set via spectral decomposition of the empirical covariance matrix.
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MapFormer: Self-Supervised Learning of Cognitive Maps with Input-Dependent Positional Embeddings
MapFormers learn cognitive maps via input-dependent Lie-algebra positional encodings and achieve near-perfect OOD generalization on cognitive tasks where standard transformers fail.
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Ordinary Least Squares is a Special Case of Transformer
Ordinary least squares is a special case of the single-layer linear transformer when attention parameters are set via spectral decomposition of the empirical covariance matrix.