A **Bidirectional GRU**, or **BiGRU**, is a sequence processing model that consists of two [GRUs](https://paperswithcode.com/method/gru). one taking the input in a forward direction, and the other in a backwards direction. It is a bidirectional recurrent neural network with only the input and forget gates.

Image Source: *Rana R (2016). Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech.*

**DeepWalk** learns embeddings (social representations) of a graph's vertices, by modeling a stream of short random walks. Social representations are latent features of the vertices that capture neighborhood similarity and community membership. These latent representations encode social relations in a continuous vector space with a relatively small number of dimensions. It generalizes neural language models to process a special language composed of a set of randomly-generated walks. 

The goal is to learn a latent representation, not only a probability distribution of node co-occurrences, and so as to introduce a mapping function $\Phi \colon v \in V \mapsto \mathbb{R}^{|V|\times d}$.
This mapping $\Phi$ represents the latent social representation associated with each vertex $v$ in the graph. In practice, $\Phi$ is represented by a $|V| \times d$ matrix of free parameters.

DeepWalk

DeepWalk: Online Learning of Social Representations

BiGRU

**Monte-Carlo Tree Search** is a planning algorithm that accumulates value estimates obtained from Monte Carlo simulations in order to successively direct simulations towards more highly-rewarded trajectories. We execute MCTS after encountering each new state to select an agent's action for that state: it is executed again to select the action for the next state. Each execution is an iterative process that simulates many trajectories starting from the current state to the terminal state. The core idea is to successively focus multiple simulations starting at the current state by extending the initial portions of trajectories that have received high evaluations from earlier simulations.

Source: Sutton and Barto, Reinforcement Learning (2nd Edition)

Image Credit: [Chaslot et al](https://www.aaai.org/Papers/AIIDE/2008/AIIDE08-036.pdf)

Year	2014
Data Source	CC BY-SA - https://paperswithcode.com