**PixelRNNs** are generative neural networks that sequentially predicts the pixels in an image along the two spatial dimensions. They model the discrete probability of the raw pixel values and encode the complete set of dependencies in the image. Variants include the Row [LSTM](https://paperswithcode.com/method/lstm) and the Diagonal [BiLSTM](https://paperswithcode.com/method/bilstm), that scale more easily to larger datasets. Pixel values are treated as discrete random variables by using a [softmax](https://paperswithcode.com/method/softmax) layer in the conditional distributions. Masked convolutions are employed to allow PixelRNNs to model full dependencies between the color channels.

There are at least eight notable examples of models from the literature that can be described using the **Message Passing Neural Networks** (**MPNN**) framework. For simplicity we describe MPNNs which operate on undirected graphs $G$ with node features $x_{v}$ and edge features $e_{vw}$. It is trivial to extend the formalism to directed multigraphs. The forward pass has two phases, a message passing phase and a readout phase. The message passing phase runs for $T$ time steps and is defined in terms of message functions $M_{t}$ and vertex update functions $U_{t}$. During the message passing phase, hidden states $h_{v}^{t}$ at each node in the graph are updated based on messages $m_{v}^{t+1}$ according to
$$
m_{v}^{t+1} = \sum_{w \in N(v)} M_{t}(h_{v}^{t}, h_{w}^{t}, e_{vw})
$$
$$
h_{v}^{t+1} = U_{t}(h_{v}^{t}, m_{v}^{t+1})
$$
where in the sum, $N(v)$ denotes the neighbors of $v$ in graph $G$. The readout phase computes a feature vector for the whole graph using some readout function $R$ according to
$$
\hat{y} = R(\\{ h_{v}^{T} | v \in G \\})
$$
The message functions $M_{t}$, vertex update functions $U_{t}$, and readout function $R$ are all learned differentiable functions. $R$ operates on the set of node states and must be invariant to permutations of the node states in order for the MPNN to be invariant to graph isomorphism.

MPNN

Neural Message Passing for Quantum Chemistry

PixelRNN

Pixel Recurrent Neural Networks

**YOLOv3** is a real-time, single-stage object detection model that builds on [YOLOv2](https://paperswithcode.com/method/yolov2) with several improvements. Improvements include the use of a new backbone network, [Darknet-53](https://paperswithcode.com/method/darknet-53) that utilises residual connections, or in the words of the author, "those newfangled residual network stuff", as well as some improvements to the bounding box prediction step, and use of three different scales from which to extract features (similar to an [FPN](https://paperswithcode.com/method/fpn)).

Source	Pixel Recurrent Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com