The **Self-Organizing Map (SOM)**, commonly also known as Kohonen network (Kohonen 1982, Kohonen 2001) is a computational method for the visualization and analysis of high-dimensional data, especially experimentally acquired information.

Extracted from [scholarpedia](http://www.scholarpedia.org/article/Self-organizing_map)

**Sources**:

Image: [scholarpedia](http://www.scholarpedia.org/article/File:Somnbc.png)

Paper: [Kohonen, T. Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)](https://doi.org/10.1007/BF00337288)

Book: [Self-Organizing Maps](https://doi.org/10.1007/978-3-642-56927-2)

**SKNet** is a type of convolutional neural network that employs [selective kernel](https://paperswithcode.com/method/selective-kernel) units, with selective kernel convolutions, in its architecture. This allows for a type of attention where the network can learn to attend to different receptive fields.

SKNet

**ProxylessNAS** directly learns neural network architectures on the target task and target hardware without any proxy task. Additional contributions include:

- Using a new path-level pruning perspective for [neural architecture search](https://paperswithcode.com/method/neural-architecture-search), showing a close connection between NAS and model compression. Memory consumption is saved by one order of magnitude by using path-level binarization.
- Using a novel gradient-based approach (latency regularization loss) for handling hardware objectives (e.g. latency). Given different hardware platforms: CPU/GPU/Mobile, ProxylessNAS enables hardware-aware neural network specialization that’s exactly optimized for the target hardware.

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