**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.

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)

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

AutoSmart

AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data

**Attribute2Font** is a model that automatically creates fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After training, the model can generate glyph images in accordance with an arbitrary set of font attribute values. A unit named Attribute Attention Module is designed to make those generated glyph images better embody the prominent font attributes. A semi-supervised learning scheme is also introduced to exploit a large number of unlabeled fonts

Source	AutoSmart: An Efficient and Automatic Machine Learning framework for Temporal Relational Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com