On Generation in Metric Spaces
Characterizes generation in metric spaces using a scale-sensitive closure dimension, showing how geometry governs the stability of generation across novelty scales.
Characterizes generation in metric spaces using a scale-sensitive closure dimension, showing how geometry governs the stability of generation across novelty scales.
We study generation in statistical learning theory and characterize when a hypothesis class supports uniform, non-uniform, or prompted generation. These characterizations use a new …