A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes in a high or infinite dimensional space, which can be used for classification, regression or other tasks. Intuitively, a good separation is achieved by the hyper-plane that has the largest distance to the nearest training data points of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier. The figure to the right shows the decision function for a linearly separable problem, with three samples on the margin boundaries, called “support vectors”. 

Source: [scikit-learn](https://scikit-learn.org/stable/modules/svm.html)

**Sparse Switchable Normalization (SSN)** is a variant on [Switchable Normalization](https://paperswithcode.com/method/switchable-normalization) where the importance ratios are constrained to be sparse. Unlike $\ell_1$ and $\ell_0$ constraints that impose difficulties in optimization, the constrained optimization problem is turned into feed-forward computation through [SparseMax](https://paperswithcode.com/method/sparsemax), which is a sparse version of [softmax](https://paperswithcode.com/method/softmax).

Sparse Switchable Normalization

SSN: Learning Sparse Switchable Normalization via SparsestMax

Mixture model network (MoNet) is a general framework allowing to design convolutional deep architectures on non-Euclidean domains such as graphs and manifolds.

Image and description from: [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://arxiv.org/pdf/1611.08402.pdf)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com