Reasoning over TKGs has two challenges: 1.

Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the permutation approaches.

The proposed predictive uncertainty, as feedback from the pre-training model, measures the confidence level of the model in the data. When fed with the chosen data, on the other hand, the pre-training model grasps an initial understanding of the new, unseen data, and at the same time attempts to remember the knowledge learned from previous data.

Such data can take numerous forms, for instance points, direction vectors, translations, or rotations, but to date there is no single architecture that can be applied to such a wide variety of geometric types while respecting their symmetries. In this paper we introduce the Geometric Algebra Transformer (GA Tr), a general-purpose architecture for geometric data.

Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks?