Attention mechanisms have made significant strides in graph learning, yet they still exhibit notable limitations: local attention faces challenges in capturing long-range information due to the inherent problems of the message-passing scheme, while global attention cannot reflect the hierarchical neighborhood structure and fails to capture fine-grained local information. In this paper, we propose a novel multihop graph attention mechanism, named Subtree Attention (STA), to address the aforementioned issues. STA seamlessly bridges the fully-attentional structure and the rooted subtree, with theoretical proof that STA approximates the global attention under extreme settings.

Should we care whether AI systems have representations of the world that are similar to those of humans? We provide an information-theoretic analysis that suggests that there should be a U-shaped relationship between the degree of representational alignment with humans and performance on few-shot learning tasks. We confirm this prediction empirically, finding such a relationship in an analysis of the performance of 491 computer vision models. We also show that highly-aligned models are more robust to both natural adversarial attacks and domain shifts. Our results suggest that human alignment is often a sufficient, but not necessary, condition for models to make effective use of limited data, be robust, and generalize well.

Clustering is a fundamental task in data science with wide-ranging applications. In k-medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics may be used; these features allow for greater interpretability of the cluster centers and the clustering of exotic objects in k-medoids clustering, respectively.

Clustering is a fundamental task in data science with wide-ranging applications. In k-medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics may be used; these features allow for greater interpretability of the cluster centers and the clustering of exotic objects in k-medoids clustering, respectively.

In this section, we show our empirical observations obtained from fine-tuning PLMs on poisoned494 datasets. Specifically, we demonstrate that the backdoor triggers are easier to learn from the lower495 layers than the features corresponding to the main task. This observation plays a pivotal role in496 designing and understanding our defense algorithm. In our experiment, we focus on the SST-2497 dataset [30] and consider the widely adopted word-level backdoor trigger and the more stealthy498 style-level trigger. For the word-level trigger, we follow the approach in prior work [25] and adopt the499 meaningless word "bb" as the trigger to minimize its impact on the original text's semantic meaning.500