Similar ideas can be found in recent developments inspired by attention, such as RWKV [Peng et al.,

A wide array of sequence models are built on a framework modeled after Transformers, comprising alternating sequence mixer and channel mixer layers.

A common approach to create more expressive GNNs is to change the message passing function of MPNNs. If a GNN is more expressive than MPNNs by adapting the message passing function, we call this non-standard message passing . Examples of this are message passing variants that operate on subgraphs [Frasca et al., 2022, Bevilacqua

The deployment of ever-larger machine learning models reflects a growing consensus that the more expressive the model class one optimizes over--and the more data one has access to--the more one can improve performance. As models get deployed in a variety of real-world scenarios, they inevitably face strategic environments.

Augmenting these graph models with topological features via persistent homology (PH) has gained prominence, but identifying the class of attributed graphs that PH can recognize remains open. We introduce a novel concept of color-separating sets to provide a complete resolution to this important problem.

This state of the art was recently critiqued by O'Bray et al.

The heads are then concatenated to form the LoRA adapter.

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