**Single-Path NAS** is a convolutional neural network architecture discovered through the Single-Path [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) approach. The NAS utilises a single-path search space. Specifically, compared to previous differentiable  NAS methods, Single-Path NAS uses one single-path over-parameterized  ConvNet to encode all architectural decisions with shared convolutional kernel parameters. The approach is built upon the  observation that different candidate convolutional operations in NAS  can be viewed as subsets of a single superkernel. Without having to  choose among different paths/operations as in multi-path methods, we instead  solve the NAS problem as finding which subset of kernel weights to use in each ConvNet layer. By sharing the convolutional kernel weights,  we encode all candidate NAS operations into a single superkernel.

The architecture itself uses the [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2) as its basic building block.

Single-path NAS

Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours

XLM-R

Unsupervised Cross-lingual Representation Learning at Scale

**GreedyNAS-A** is a convolutional neural network discovered using the [GreedyNAS](https://paperswithcode.com/method/greedynas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) method. The basic building blocks used are inverted residual blocks (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)) and squeeze-and-excitation blocks.

Source	Unsupervised Cross-lingual Representation Learning at Scale
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

Viet-Anh on Software

What is: XLM-R?

Viet-Anh on Software