The core ingredient of **CayleyNet** is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. The model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators.

Description adapted from: [CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters](https://arxiv.org/pdf/1705.07664.pdf)

**GroIE** is an RoI extractor which intends to overcome the limitation of existing extractors which select only one (the best) layer from the [FPN](https://paperswithcode.com/method/fpn). The intuition is that all the layers of FPN retain useful
information. Therefore, the proposed layer introduces non-local building blocks and attention mechanisms to boost the performance.

GRoIE

A novel Region of Interest Extraction Layer for Instance Segmentation

CayleyNet

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

FT-Transformer (Feature Tokenizer + Transformer) is a simple adaptation of the [Transformer](/method/transformer) architecture for the tabular domain. The model (Feature Tokenizer component) transforms all features (categorical and numerical) to tokens and runs a stack of Transformer layers over the tokens, so every Transformer layer operates on the feature level of one object. (This model is similar to [AutoInt](/method/autoint)). In the Transformer component, the `[CLS]` token is appended to $T$. Then $L$ Transformer layers are applied. PreNorm is used for easier optimization and good performance. The final representation of the `[CLS]` token is used for prediction.

Source	CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com