Scalable method to train large scale GNN models via sampling small subgraphs.

**Adaptive Softmax** is a speedup technique for the computation of probability distributions over words. The adaptive [softmax](https://paperswithcode.com/method/softmax) is inspired by the class-based [hierarchical softmax](https://paperswithcode.com/method/hierarchical-softmax), where the word classes are built to minimize the computation time. Adaptive softmax achieves efficiency by explicitly taking into account the computation time of matrix-multiplication on parallel systems and combining it with a few important observations, namely keeping a shortlist of frequent words in the root node
and reducing the capacity of rare words.

Adaptive Softmax

Efficient softmax approximation for GPUs

GraphSAINT

GraphSAINT: Graph Sampling Based Inductive Learning Method

**ScanSSD** is a single-shot Detector ([SSD](https://paperswithcode.com/method/ssd)) for locating math formulas offset from text and embedded in textlines. It uses only visual features for detection: no formatting or typesetting information such as layout, font, or character labels are employed. Given a 600 dpi document page image, a Single Shot Detector (SSD) locates formulas at multiple scales using sliding windows, after which candidate detections are pooled to obtain page-level results.

Source	GraphSAINT: Graph Sampling Based Inductive Learning Method
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com