[Transformer](https://paperswithcode.com/method/transformer)-Decoder is a modification to Transformer-Encoder-Decoder for long sequences that drops the encoder
module, combines the input and output sequences into a single ”sentence” and is trained as a standard language model. It is used in [GPT](https://paperswithcode.com/method/gpt) and later revisions.

**Prediction-aware One-To-One**, or **POTO**, is an assignment rule for object detection which dynamically assigns the foreground samples according to the quality of classification and regression simultaneously.

POTO

End-to-End Object Detection with Fully Convolutional Network

Generating Wikipedia by Summarizing Long Sequences

**RotatE** is a method for generating graph embeddings which is able to model and infer various relation patterns including: symmetry/antisymmetry, inversion, and composition. Specifically, the RotatE model defines each relation as a rotation from the source entity to the target entity in the complex vector space. The RotatE model is trained using a [self-adversarial negative sampling](https://paperswithcode.com/method/self-adversarial-negative-sampling) technique.

Source	Generating Wikipedia by Summarizing Long Sequences
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com