A causal autoregressive buffer enables efficient batched autoregressive sampling and joint density evaluation in set-based transformer models by caching context and attending to prior predictions.
We then sampleV level ∼Uniform{0,1,2}, representing the perceptual noise associated withs V
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Efficient Autoregressive Inference for Transformer Probabilistic Models
A causal autoregressive buffer enables efficient batched autoregressive sampling and joint density evaluation in set-based transformer models by caching context and attending to prior predictions.