**NAS-FPN** is a Feature Pyramid Network that is discovered via [Neural Architecture Search](https://paperswithcode.com/method/neural-architecture-search) in a novel scalable search space covering all cross-scale connections. The discovered architecture consists of a combination of top-down and bottom-up connections to fuse features across scales

A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.

Intuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).

Image Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)

Convolution

NAS-FPN

NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection

**FastMoE ** is a distributed MoE training system based on PyTorch with common accelerators. The system provides a hierarchical interface for both flexible model design and adaption to different applications, such as [Transformer-XL](https://paperswithcode.com/method/transformer-xl) and Megatron-LM.

Source	NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com