Recurrent Event Network (RE-NET) is an autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. RE-NET employs a recurrent event encoder to encode past facts and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules.

Stochastic Steady-state Embedding (SSE) is an algorithm that can learn many steady-state algorithms over graphs. Different from graph neural network family models, SSE is trained stochastically which only requires 1-hop information, but can capture fixed point relationships efficiently and effectively.

Description and Image from: [Learning Steady-States of Iterative Algorithms over Graphs](https://proceedings.mlr.press/v80/dai18a.html)

Learning Steady-States of Iterative Algorithms over Graphs

RE-NET

Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs

Deformable ConvNets do not learn an affine transformation. They divide convolution into two steps, firstly sampling features on a regular grid $ \mathcal{R} $ from the input feature map, then aggregating sampled features by weighted summation using a convolution kernel. The process can be written as:
\begin{align}
    Y(p_{0}) &= \sum_{p_i \in \mathcal{R}} w(p_{i}) X(p_{0} + p_{i})
\end{align}
\begin{align}
    \mathcal{R}  &= \{(-1,-1), (-1, 0), \dots, (1, 1)\}
\end{align}
The deformable convolution augments the sampling process by introducing a group of learnable offsets $\Delta p_{i}$ which can be generated by a lightweight CNN. Using the offsets $\Delta p_{i}$, the deformable convolution can be formulated as:
\begin{align}
    Y(p_{0}) &= \sum_{p_i \in \mathcal{R}} w(p_{i}) X(p_{0} + p_{i} + \Delta p_{i}). 
\end{align}
Through the above method, adaptive sampling is achieved.
However, $\Delta p_{i}$ is a floating point value
unsuited to grid sampling. 
To address this problem, bilinear interpolation is used. Deformable RoI pooling is also used, which greatly improves object detection. 

Deformable ConvNets adaptively select the important regions and enlarge the valid receptive field of convolutional neural networks; this is important in object detection and semantic segmentation tasks.

Source	Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com