In Section 2.2 we show, using an idealized model for the token distribution, that if one iteratively

Another possible reason is that it is unclear if the low signal-to-noise ratio of neuroimaging tools such as functional Magnetic Resonance Imaging (fMRI) can allow us to reveal the correlates of complex (and perhaps subtle) syntactic representations.

In particular, we apply this approach to build CUBE (CUltural BEnchmark forText-to-Image models), afirst-of-its-kind benchmark toevaluate cultural competence of T2I models.2 CUBE covers cultural artifacts associated with 8 countries across different geo-cultural regions and along 3 concepts: cuisine, landmarks, and art. CUBE consists of 1) CUBE-1K, a set of high-quality prompts thatenable theevaluation ofcultural awareness, and2)CUBE-CSpace, a larger dataset of cultural artifacts that serves as grounding to evaluate cultural diversity.

Recently many algorithms were devised for reinforcement learning (RL) with functionapproximation.

This metric computes the variance of the mean of the latent encoding of the data points in each dimension of the latent space,Var(µφ(x)i), wherexis sampledfromthedataset. For hierarchical architectures the reported number is for thez2 which is the highest stochasticlayer. Toregularize the Exemplar VAE, we used leave-one-out and exemplar sub-sampling. That is why did not compare directly against a mixture model prior in the primary experimental section. Three different architectures are used in the experiments, described below.