Spatio-Temporal Neural Networks are artificial intelligence models designed to process data that changes over both space and time. They are particularly good at understanding patterns where the position and timing of data points matter, such as videos, traffic flows, or weather patterns. These networks combine techniques for handling spatial data, like images or maps, with…
Category: Deep Learning
Temporal Convolutional Networks
Temporal Convolutional Networks, or TCNs, are a type of neural network designed to handle data that changes over time, such as sequences or time series. Instead of processing one step at a time like some models, TCNs use convolutional layers to look at several steps in the sequence at once, which helps them spot patterns…
Graph-Based Sequence Modelling
Graph-based sequence modelling is a method used to understand and predict series of events or data points by representing them as nodes and connections in a graph structure. This approach allows for capturing complex relationships and dependencies that may not follow a simple, straight line. By using graphs, it becomes easier to analyse sequences where…
Hierarchical Attention Networks
Hierarchical Attention Networks (HANs) are a type of neural network model designed to process and understand data with a natural hierarchical structure, such as documents made up of sentences and words. HANs use attention mechanisms at multiple levels, typically first focusing on which words in a sentence are important, then which sentences in a document…
Modular Neural Network Design
Modular neural network design is an approach to building artificial neural networks by dividing the overall system into smaller, independent modules. Each module is responsible for a specific part of the task or problem, and the modules work together to solve the whole problem. This method makes it easier to manage, understand and improve complex…
Efficient Transformer Variants
Efficient Transformer variants are modified versions of the original Transformer model designed to use less memory and computation. Traditional Transformers can be slow and expensive when working with long texts or large datasets. These variants use clever techniques to make the models faster and less resource-intensive while aiming to keep their accuracy high.
Neural Network Quantisation Techniques
Neural network quantisation techniques are methods used to reduce the size and complexity of neural networks by representing their weights and activations with fewer bits. This makes the models use less memory and run faster on hardware with limited resources. Quantisation is especially valuable for deploying models on mobile devices, embedded systems, or any place…
Multi-Scale Feature Learning
Multi-scale feature learning is a technique in machine learning where a model is designed to understand information at different levels of detail. This means it can recognise both small, fine features and larger, more general patterns within data. It is especially common in areas like image and signal processing, where objects or patterns can appear…
Hybrid CNN-RNN Architectures
Hybrid CNN-RNN architectures combine two types of neural networks: convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs are good at recognising patterns and features in data like images, while RNNs are designed to handle sequences, such as text or audio. By joining them, these architectures can process both spatial and temporal information, making…
Knowledge Transfer in Multi-Domain Learning
Knowledge transfer in multi-domain learning refers to using information or skills learned in one area to help learning or performance in another area. This approach allows a system, like a machine learning model, to apply what it has learned in one domain to new, different domains. It helps reduce the need for large amounts of…