This is **Graph Transformer** method, proposed as a generalization of [Transformer](https://paperswithcode.com/method/transformer) Neural Network architectures, for arbitrary graphs.

Compared to the original Transformer, the highlights of the presented architecture are:

- The attention mechanism is a function of neighborhood connectivity for each node in the graph.  
- The position encoding is represented by Laplacian eigenvectors, which naturally generalize the sinusoidal positional encodings often used in NLP.  
- The [layer normalization](https://paperswithcode.com/method/layer-normalization) is replaced by a [batch normalization](https://paperswithcode.com/method/batch-normalization) layer.  
- The architecture is extended to have edge representation, which can be critical to tasks with rich information on the edges, or pairwise interactions (such as bond types in molecules, or relationship type in KGs. etc).

**AdamW** is a stochastic optimization method that modifies the typical implementation of weight decay in [Adam](https://paperswithcode.com/method/adam), by decoupling [weight decay](https://paperswithcode.com/method/weight-decay) from the gradient update. To see this, $L\_{2}$ regularization in Adam is usually implemented with the below modification where $w\_{t}$ is the rate of the weight decay at time $t$:

$$ g\_{t} = \nabla{f\left(\theta\_{t}\right)} + w\_{t}\theta\_{t}$$

while AdamW adjusts the weight decay term to appear in the gradient update:

$$ \theta\_{t+1, i} = \theta\_{t, i} - \eta\left(\frac{1}{\sqrt{\hat{v}\_{t} + \epsilon}}\cdot{\hat{m}\_{t}} + w\_{t, i}\theta\_{t, i}\right), \forall{t}$$

AdamW

Decoupled Weight Decay Regularization

Graph Transformer

A Generalization of Transformer Networks to Graphs

**YOLOv2**, or [**YOLO9000**](https://www.youtube.com/watch?v=QsDDXSmGJZA), is a single-stage real-time object detection model. It improves upon [YOLOv1](https://paperswithcode.com/method/yolov1) in several ways, including the use of [Darknet-19](https://paperswithcode.com/method/darknet-19) as a backbone, [batch normalization](https://paperswithcode.com/method/batch-normalization), use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.

Source	A Generalization of Transformer Networks to Graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com