A **Graph Attention Network (GAT)** is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, a GAT enables (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront.

See [here](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/9_gat.html) for an explanation by DGL.

ComplEx model trained with a nuclear norm regularizer; A relation prediction objective is added on top of the commonly used 1vsAll objective.

ComplEx-N3-RP

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

Graph Attention Networks

**VGG Loss** is a type of content loss introduced in the [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://paperswithcode.com/paper/perceptual-losses-for-real-time-style) super-resolution and style transfer framework. It is an alternative to pixel-wise losses; VGG Loss attempts to be closer to perceptual similarity. The [VGG](https://paperswithcode.com/method/vgg) loss is based on the [ReLU](https://paperswithcode.com/method/relu) activation layers of the pre-trained 19 layer VGG network. With $\phi\_{i,j}$ we indicate the feature map obtained by the $j$-th [convolution](https://paperswithcode.com/method/convolution) (after activation) before the $i$-th maxpooling layer within the VGG19 network, which we consider given. We then define the VGG loss as the euclidean distance between the feature representations of a reconstructed image $G\_{\theta\_{G}}\left(I^{LR}\right)$ and the reference image $I^{HR}$:

$$ l\_{VGG/i.j} = \frac{1}{W\_{i,j}H\_{i,j}}\sum\_{x=1}^{W\_{i,j}}\sum\_{y=1}^{H\_{i,j}}\left(\phi\_{i,j}\left(I^{HR}\right)\_{x, y} - \phi\_{i,j}\left(G\_{\theta\_{G}}\left(I^{LR}\right)\right)\_{x, y}\right)^{2}$$ 

Here $W\_{i,j}$ and $H\_{i,j}$ describe the dimensions of the respective feature maps within the VGG network.

Source	Graph Attention Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com