Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
Tabpfn: A transformer that solves small tabular classification problems in a second, 2023
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SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.
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Latent Chain-of-Thought Improves Structured-Data Transformers
Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
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Simulation as Supervision: Mechanistic Pretraining for Scientific Discovery
SGNNs pretrain neural networks on synthetic corpora from multiple mechanistic models and noise levels to enable robust forecasting and back-to-simulation attribution across epidemiology, ecology, and other fields.