Motivated by this property, we propose a method to extend transformers to tree-structured data, enabling sequence-totree, tree-to-sequence, and tree-to-tree mappings. Our approach abstracts the transformer'ssinusoidal positional encodings, allowing ustoinstead useanovel positional encoding scheme to represent node positions within trees.

Unsupervised domain adaptation (UDA) aims to enhance the generalization capability ofacertain model from asource domain toatargetdomain.

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The supplementary material is organized as follows: A: more details on the efficient implementation; B: additional graphical illustration; C: more performance comparisons; D: additional visualization results; E: discussions. Since there are no loops in the tree, the shortest path between any two vertices is unique. To facilitate a better comprehension, we provide a detailed graphical illustration in Fig. A1 to describe In the implementation, it is unnecessary to compute as it explicitly. Figure A1: The graphical illustration of the detailed process of global affinity propagation. The experimental results are shown in Table A1.

In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width oftwocombines thebest ofboth synchronous and asynchronous training.