CP with N3 Regularizer and Relation Prediction

**BLOOM** is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of
sources in 46 natural and 13 programming languages (59 in total).

BLOOM

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

CP-N3-RP

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

**Non Maximum Suppression** is a computer vision method that selects a single entity out of many overlapping entities (for example bounding boxes in object detection). The criteria is usually discarding entities that are below a given probability bound. With remaining entities we repeatedly pick the entity with the highest probability, output that as the prediction, and discard any remaining box where a $\text{IoU} \geq 0.5$ with the box output in the previous step.

Image Credit: [Martin Kersner](https://github.com/martinkersner/non-maximum-suppression-cpp)

Source	Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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