The Legendre Memory Unit (LMU) is mathematically derived to orthogonalize
its continuous-time history – doing so by solving d coupled ordinary differential
equations (ODEs), whose phase space linearly maps onto sliding windows of
time via the Legendre polynomials up to degree d-1.  It is optimal for compressing temporal information.

See paper for equations (markdown isn't working).

Official github repo: [https://github.com/abr/lmu](https://github.com/abr/lmu)

**Thinned U-shape Module**, or **TUM**, is a feature extraction block used for object detection models. It was introduced as part of the [M2Det](https://paperswithcode.com/method/m2det) architecture. Different from [FPN](https://paperswithcode.com/method/fpn) and [RetinaNet](https://paperswithcode.com/method/retinanet), TUM adopts a thinner U-shape structure as illustrated in the Figure to the right. The encoder is a series of 3x3 [convolution](https://paperswithcode.com/method/convolution) layers with stride 2. And the decoder takes the outputs of these layers as its reference set of feature maps, while the original FPN chooses the output of the last layer of each stage in [ResNet](https://paperswithcode.com/method/resnet) backbone. 

In addition, with TUM, we add [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) layers after the upsample and element-wise sum operation at the decoder branch to enhance learning ability and keep smoothness for the features. In the context of M2Det, all of the outputs in the decoder of each TUM form the multi-scale features of the current level. As a whole, the outputs of stacked TUMs form the multi-level multi-scale features, while the front TUM mainly provides shallow-level features, the middle TUM provides medium-level features, and the back TUM provides deep-level features.

M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network

Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks

**AutoSmart** is AutoML framework for temporal relational data. The framework includes automatic data processing, table merging, feature engineering, and model tuning, integrated with a time&memory control unit.

Source	Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com