Temporal knowledge modelling is a way of organising information that changes over time. It helps computers and people understand not just facts, but also when those facts are true or relevant. This approach allows systems to keep track of events, sequences, and the duration of different states or relationships. For example, a person’s job history…
Category: Embeddings & Representations
Graph Knowledge Propagation
Graph knowledge propagation is a process where information or attributes are shared between connected nodes in a network, such as people in a social network or web pages on the internet. This sharing helps each node gain knowledge from its neighbours, allowing the system to learn or infer new relationships and properties. It is widely…
Neural Feature Disentanglement
Neural feature disentanglement is a process in machine learning where a model learns to separate different underlying factors or characteristics from data. Instead of mixing all the information together, the model creates distinct representations for each important feature, such as colour, shape, or size in images. This helps the model to better understand and manipulate…
Knowledge Representation Models
Knowledge representation models are ways for computers to organise, store, and use information so they can reason and solve problems. These models help machines understand relationships, rules, and facts in a structured format. Common types include semantic networks, frames, and logic-based systems, each designed to make information easier for computers to process and work with.
Cross-Modal Knowledge Transfer
Cross-modal knowledge transfer is a technique where learning or information from one type of data, like images, is used to improve understanding or performance with another type, such as text or sound. This approach allows systems to apply what they have learned in one area to help with tasks in a different area. It is…
Semantic Inference Models
Semantic inference models are computer systems designed to understand the meaning behind words and sentences. They analyse text to determine relationships, draw conclusions, or identify implied information that is not directly stated. These models rely on patterns in language and large datasets to interpret subtle or complex meanings, making them useful for tasks like question…
Graph-Based Feature Extraction
Graph-based feature extraction is a method used to identify and describe important characteristics or patterns from data that can be represented as a network or graph. In this approach, data points are seen as nodes and their relationships as edges, allowing complex connections to be analysed. Features such as node connectivity, centrality, or community structure…
Contrastive Feature Learning
Contrastive feature learning is a machine learning approach that helps computers learn to tell the difference between similar and dissimilar data points. The main idea is to teach a model to bring similar items closer together and push dissimilar items further apart in its understanding. This method does not rely heavily on labelled data, making…
Cross-Task Generalization
Cross-task generalisation is the ability of a system, usually artificial intelligence, to apply what it has learned from one task to different but related tasks. This means a model does not need to be retrained from scratch for every new problem if the tasks share similarities. It helps create more flexible and adaptable AI that…
Sparse Feature Extraction
Sparse feature extraction is a technique in data analysis and machine learning that focuses on identifying and using only the most important or relevant pieces of information from a larger set of features. Rather than working with every possible detail, it selects a smaller number of features that best represent the data. This approach helps…