**Geomancer** is a nonparametric algorithm for symmetry-based disentangling of data manifolds. It learns a set of subspaces to assign to each point in the dataset, where each subspace is the tangent space of one disentangled submanifold. This means that geomancer can be used to disentangle manifolds for which there may not be a global axis-aligned coordinate system.

**GCNII** is an extension of a [Graph Convolution Networks](https://www.paperswithcode.com/method/gcn) with two new techniques, initial residual and identify mapping, to tackle the problem of oversmoothing -- where stacking more layers and adding non-linearity tends to degrade performance. At each layer, initial residual constructs a skip connection from the input layer, while identity mapping adds an identity matrix to the weight matrix.

GCNII

Simple and Deep Graph Convolutional Networks

GEOMANCER

Disentangling by Subspace Diffusion

ARM-Net is an adaptive relation modeling network tailored for structured data, and a lightweight framework ARMOR based on ARM-Net for relational data analytics. The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature. The authors propose a novel sparse attention mechanism to dynamically generate the interaction weights given the input tuple, so that we can explicitly model cross features of arbitrary orders with noisy features filtered selectively. Then during model inference, ARM-Net can specify the cross features being used for each prediction for higher accuracy and better interpretability.

Source	Disentangling by Subspace Diffusion
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com