Graph signal extraction is the process of identifying and isolating meaningful patterns or information from data that is organised on a network or graph. In such data, each node in the graph has a value, and these values can represent anything from sensor readings to social media activity. The aim is to filter out noise…
Graph feature modelling is the process of identifying and using important characteristics or patterns from data that are represented as graphs. In graphs, data points are shown as nodes, and the connections between them are called edges. By extracting features from these nodes and edges, such as how many connections a node has or how…
Neural Representation Analysis is a method used to understand how information is processed and stored within the brain or artificial neural networks. It examines the patterns of activity across groups of neurons or network units when responding to different stimuli or performing tasks. By analysing these patterns, researchers can learn what kind of information is…
Quantum feature analysis is a method that uses quantum computing to study and process features or characteristics in data. It helps to identify which parts of the data are most important for tasks like classification or prediction. By using quantum algorithms, this analysis can sometimes handle complex data patterns more efficiently than classical methods.
Neural representation optimisation involves improving how information is encoded and processed within a neural network. This process focuses on making the network’s internal representations more effective so it can learn patterns and make decisions more accurately. Techniques include adjusting the network’s structure, training methods, or using special loss functions to encourage more meaningful or efficient…
Graph signal modelling is the process of representing and analysing data that is linked to the nodes or edges of a graph. This type of data can show how values change across a network, such as traffic speeds on roads or temperatures at different points in a sensor network. By using graph signal modelling, we…
Neural representation analysis is a method used to understand how information is encoded and processed in the brain or artificial neural networks. By examining patterns of activity, researchers can learn which features or concepts are represented and how different inputs or tasks change these patterns. This helps to uncover the internal workings of both biological…
Neural representation tuning refers to the way that artificial neural networks adjust the way they represent and process information in response to data. During training, the network changes the strength of its connections so that certain patterns or features in the data become more strongly recognised by specific neurons. This process helps the network become…
Graph signal analysis is a method for studying data that is spread over the nodes of a graph, such as sensors in a network or users in a social network. It combines ideas from signal processing and graph theory to understand how data values change and interact across connected points. This approach helps identify patterns,…
Quantum feature analysis is a process that uses quantum computing techniques to examine and interpret the important characteristics, or features, in data. It aims to identify which parts of the data are most useful for making predictions or decisions. This method takes advantage of quantum systems to analyse information in ways that can be faster…