Meta-learning optimisation is a machine learning approach that focuses on teaching models how to learn more effectively. Instead of training a model for a single task, meta-learning aims to create models that can quickly adapt to new tasks with minimal data. This is achieved by optimising the learning process itself, so the model becomes better…
Knowledge propagation models describe how information, ideas, or skills spread within a group, network, or community. These models help researchers and organisations predict how quickly and widely knowledge will transfer between people. They are often used to improve learning, communication, and innovation by understanding the flow of knowledge.
Incremental learning strategies are methods that allow a system or individual to learn new information gradually, building upon existing knowledge without needing to start over each time. This approach is common in both human learning and machine learning, where new data is incorporated step by step. Incremental learning helps in efficiently updating knowledge without forgetting…
Adaptive neural architectures are artificial intelligence systems designed to change their structure or behaviour based on the task or data they encounter. Unlike traditional neural networks that have a fixed design, these systems can adjust aspects such as the number of layers, types of connections, or processing strategies while learning or during operation. This flexibility…
Neural Module Orchestration is a method in artificial intelligence where different specialised neural network components, called modules, are combined and coordinated to solve complex problems. Each module is designed for a specific task, such as recognising images, understanding text, or making decisions. By orchestrating these modules, a system can tackle tasks that are too complicated…
Domain-invariant representations are ways of encoding data so that important features remain the same, even if the data comes from different sources or environments. This helps machine learning models perform well when they encounter new data that looks different from what they were trained on. The goal is to focus on what matters for a…
Knowledge-driven inference is a method where computers or systems use existing knowledge, such as rules or facts, to draw conclusions or make decisions. Instead of relying only on patterns in data, these systems apply logic and structured information to infer new insights. This approach is common in expert systems, artificial intelligence, and data analysis where…
Causal effect modelling is a way to figure out if one thing actually causes another, rather than just being associated with it. It uses statistical tools and careful study design to separate true cause-and-effect relationships from mere coincidences. This helps researchers and decision-makers understand what will happen if they change something, like introducing a new…
Uncertainty calibration methods are techniques used to ensure that a model’s confidence in its predictions matches how often those predictions are correct. In other words, if a model says it is 80 percent sure about something, it should be right about 80 percent of the time when it makes such predictions. These methods help improve…
Neural disentanglement metrics are tools used to measure how well a neural network has separated different factors or features within its learned representations. These metrics help researchers understand if the network can distinguish between different aspects, such as shape and colour, in the data it processes. By evaluating disentanglement, scientists can improve models to make…