Autoregressive Sampling
A core mechanism of a class of generative models in which the model predicts the next element of a sequence one by one based on all previously generated elements; its mathematical basis is the chain rule, which decomposes a joint probability into a product of conditional probabilities. Representative models include the GPT series, PixelCNN and WaveNet.
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Autoregressive sampling is essentially learning statistical correlations rather than causal mechanisms, which leads to a fundamental bottleneck in physical tasks.
Autoregressive sampling is the core mechanism of generative models such as the GPT series of language models, PixelCNN, and WaveNet.
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Autoregressive sampling is essentially learning statistical correlations rather than causal mechanisms, which leads to a fundamental bottleneck in physical tasks.
50%UnverifiedAutoregressive sampling is the core mechanism of generative models such as the GPT series of language models, PixelCNN, and WaveNet.
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