Symbolic regression is a type of machine learning that tries to find mathematical equations that best fit a set of data. Instead of just adjusting numbers in a fixed equation, symbolic regression searches for both the structure and the parameters of equations. This means it can suggest entirely new formulas that describe how inputs relate…
Temporal Graph Networks are a type of machine learning model that analyse data where relationships between items change over time. These models track not only the connections between objects, like people or devices, but also how these connections appear, disappear, or change as time passes. This helps to understand patterns and predict future events in…
Spectral graph theory studies the properties of graphs using the mathematics of matrices and their eigenvalues. It looks at how the structure of a network is reflected in the numbers that come from its adjacency or Laplacian matrices. This approach helps to reveal patterns, connections, and clusters within networks that might not be obvious at…
Curiosity-driven exploration is a method where a person or a computer system actively seeks out new things to learn or experience, guided by what seems interesting or unfamiliar. Instead of following strict instructions or rewards, the focus is on exploring unknown areas or ideas out of curiosity. This approach is often used in artificial intelligence…
Structured prediction is a type of machine learning where the goal is to predict complex outputs that have internal structure, such as sequences, trees, or grids. Unlike simple classification or regression, where each prediction is a single value or label, structured prediction models outputs that are made up of multiple related elements. This approach is…
Knowledge transferability is the ability to apply what has been learned in one situation to a different context or problem. It means that skills, information, or methods are not limited to their original use but can help solve new challenges. This concept is important in education, technology, and the workplace, as it helps people and…
Knowledge injection is the process of adding specific information or facts into an artificial intelligence system, such as a chatbot or language model, to improve its accuracy or performance. This can be done by directly feeding the system extra data, rules, or context that it would not otherwise have known. Knowledge injection helps AI systems…
Synthetic feature generation is the process of creating new data features from existing ones to help improve the performance of machine learning models. These new features are not collected directly but are derived by combining, transforming, or otherwise manipulating the original data. This helps models find patterns that may not be obvious in the raw…
Cross-validation techniques are methods used to assess how well a machine learning model will perform on information it has not seen before. By splitting the available data into several parts, or folds, these techniques help ensure that the model is not just memorising the training data but is learning patterns that generalise to new data….
Robust optimisation is a method in decision-making and mathematical modelling that aims to find solutions that perform well even when there is uncertainty or variability in the input data. Instead of assuming that all information is precise, it prepares for worst-case scenarios by building in a margin of safety. This approach helps ensure that the…