Meanwhile, a Neural V ariational Inference (NVI) approach is proposed to learn our model with graph neural networks to encode the document graphs. Besides, we theoretically demonstrate that Latent Dirichlet Allocation (LDA) can be derived from GNTM as a special case with similar objective functions.

By assessing human annotators as classifiers, we remove the subjective quality of existing evaluation metrics.

However, this may not be possible in discrete time, which is often the case in practical learning and computation.

Our novel method, called LookHere, provides translation-equivariance, ensures attention head diversity, and limits the distribution shift that attention heads face when extrapolating. We demonstrate that LookHere improves performance on classification (avg.

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KL divergence between the trained posterior distribution obtained by infinitesimal step size gradient descent and a Gaussian prior.