Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.
Transformers as statisticians: Provable in-context learning with in-context algorithm selection.ArXiv, abs/2306.04637
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STNPs extend TNPs with a spectral aggregator that estimates context spectra, forms spectral mixtures, and injects task-adaptive frequency features to better handle periodicity.
Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
In-context learning shows persistent interference from prior examples, with more misleading linear examples degrading quadratic predictions and training curricula modulating recovery speed.
citing papers explorer
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Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
Transformers trained to imitate Bayesian posterior Neyman allocations achieve smoothness-adaptive ATE estimation via mixture-of-experts in-context learning.
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Spectral Transformer Neural Processes
STNPs extend TNPs with a spectral aggregator that estimates context spectra, forms spectral mixtures, and injects task-adaptive frequency features to better handle periodicity.
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Visual prompting reimagined: The power of the Activation Prompts
Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
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When Context Sticks: Studying Interference in In-Context Learning
In-context learning shows persistent interference from prior examples, with more misleading linear examples degrading quadratic predictions and training curricula modulating recovery speed.