**AdaGPR** is an adaptive, layer-wise graph [convolution](https://paperswithcode.com/method/convolution) model. AdaGPR applies adaptive generalized Pageranks at each layer of a [GCNII](https://paperswithcode.com/method/gcnii) model by learning to predict the coefficients of generalized Pageranks using sparse solvers.

**AutoInt** is a deep tabular learning method that models high-order feature interactions of input features. AutoInt can be applied to both numerical and categorical input features. Specifically, both the numerical and categorical features are mapped into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled.

AutoInt

AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

AdaGPR

Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank

A **trainable 3D interaction space** aims to captures the associations between the triplet components and helps model the recognition of multiple triplets in the same frame.

Source: [Nwoye et al.](https://arxiv.org/pdf/2007.05405v1.pdf)

Image source: [Nwoye et al.](https://arxiv.org/pdf/2007.05405v1.pdf)

Source	Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com