Out-of-domain (OOD) generalization is a significant challenge for machine learning models.

Inpractice,wealso need to consider the lower numerical precision of GPU implementation in model training, which further deteriorates the stability.

Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy.

Nevertheless, these methods usually rely on hand-crafted or external subgraph construction, andthesubgraph assembling depends solely onlocal arrangement.

Topic model evaluation, like evaluation of other unsupervised methods, can be contentious.

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We also nuance the idea that learning happens at the edge of chaos by giving evidence that avery desirable feature forneural networks isthehyperbolicity of their Jacobian at initialization.