Inthis methods families optimization 71], etc. canbe between Non-nested In variables stochastic are x/ p

Attheheart offontgeneration liestheextraction ofstyles from some reference glyphs ofacertain font, and generate the remaining glyphs of this font.

Fora ofourmodelto PMMRand MMR-IV, see Table 1.

KL divergence estimation from samples was studied thoroughly by Nguyen et al. [1] using a variational technique, convex optimization and RKHS norm regularization, while also providing theoretical guarantees and insights.

While most existing approaches (e.g., prompt engineering) focus on the LLM's learned representations to patch this performance gap, our experiments actually reveal that LLM representations contain sufficient information to make good