**Social-STGCNN** is a method for human trajectory prediction. Pedestrian trajectories are not only influenced by the pedestrian itself but also by interaction with surrounding objects.

A **Parametric Rectified Linear Unit**, or **PReLU**, is an activation function that generalizes the traditional rectified unit with a slope for negative values. Formally:

$$f\left(y\_{i}\right) = y\_{i} \text{ if } y\_{i} \ge 0$$
$$f\left(y\_{i}\right) = a\_{i}y\_{i} \text{ if } y\_{i} \leq 0$$

The intuition is that different layers may require different types of nonlinearity. Indeed the authors find in experiments with convolutional neural networks that PReLus for the initial layer have more positive slopes, i.e. closer to linear. Since the filters of the first layers are Gabor-like filters such as edge or texture detectors, this shows a circumstance where positive and negative responses of filters are respected. In contrast the authors find deeper layers have smaller coefficients, suggesting the model becomes more discriminative at later layers (while it wants to retain more information at earlier layers).

PReLU

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Social-STGCNN

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

A **Neural Turing Machine** is a working memory neural network model. It couples a neural network architecture with external memory resources. The whole architecture is differentiable end-to-end with gradient descent. The models can infer tasks such as copying, sorting and associative recall.

A Neural Turing Machine (NTM) architecture contains two basic components: a neural
network controller and a memory bank. The Figure presents a high-level diagram of the NTM
architecture. Like most neural networks, the controller interacts with the external world via
input and output vectors. Unlike a standard network, it also interacts with a memory matrix
using selective read and write operations. By analogy to the Turing machine we refer to the
network outputs that parameterise these operations as “heads.”

Every component of the architecture is differentiable. This is achieved by defining 'blurry' read and write operations that interact to a greater or lesser degree with all the elements in memory (rather
than addressing a single element, as in a normal Turing machine or digital computer). The
degree of blurriness is determined by an attentional “focus” mechanism that constrains each
read and write operation to interact with a small portion of the memory, while ignoring the
rest. Because interaction with the memory is highly sparse, the NTM is biased towards
storing data without interference. The memory location brought into attentional focus is
determined by specialised outputs emitted by the heads. These outputs define a normalised
weighting over the rows in the memory matrix (referred to as memory “locations”). Each
weighting, one per read or write head, defines the degree to which the head reads or writes
at each location. A head can thereby attend sharply to the memory at a single location or
weakly to the memory at many locations

Source	Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com