Evolving lifted data vectors under a chaotic dynamical system before softmax classification accelerates training and improves accuracy over standard and lifted-only baselines on perturbed orthogonal vectors.
Predictability: A problem partly solved
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.
citing papers explorer
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Enhancing classification accuracy through chaos
Evolving lifted data vectors under a chaotic dynamical system before softmax classification accelerates training and improves accuracy over standard and lifted-only baselines on perturbed orthogonal vectors.
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Transformers for dynamical systems learn transfer operators in-context
Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.