VERtex Similarity Embeddings (VERSE) is a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network.

Source: [Tsitsulin et al.](https://arxiv.org/pdf/1803.04742v1.pdf)

Image source: [Tsitsulin et al.](https://arxiv.org/pdf/1803.04742v1.pdf)

An **Recursive Feature Pyramid (RFP)** builds on top of the Feature Pyramid Networks ([FPN](https://paperswithcode.com/method/fpn)) by incorporating extra feedback connections from the FPN layers into the bottom-up backbone layers. Unrolling the recursive structure to a sequential implementation, we obtain a backbone for object detector that looks at the images twice or more. Similar to the cascaded detector heads in [Cascade R-CNN](https://paperswithcode.com/method/cascade-r-cnn) trained with more selective examples, an RFP recursively enhances FPN to generate increasingly powerful representations. Resembling Deeply-Supervised Nets, the feedback connections bring the features that directly receive gradients from the detector heads back to the low levels of the bottom-up backbone to speed up training and boost performance.

DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution

VERSE

VERSE: Versatile Graph Embeddings from Similarity Measures

**ENIGMA** is an evaluation framework for dialog systems based on Pearson and Spearman's rank correlations between the estimated rewards and the true rewards.  ENIGMA only requires a handful of pre-collected experience data, and therefore does not involve human interaction with the target policy during the evaluation, making automatic evaluations feasible. More importantly, ENIGMA is model-free and agnostic to the behavior policies for collecting the experience data (see details in Section 2), which significantly alleviates the technical difficulties of modeling complex dialogue environments and human behaviors.

Source	VERSE: Versatile Graph Embeddings from Similarity Measures
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: VERtex Similarity Embeddings?

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