Domain-agnostic learning is a machine learning approach where models are designed to work across different fields or types of data without being specifically trained for one area. This means the system can handle information from various sources, like text, images, or numbers, and still perform well. The goal is to create flexible tools that do…
Category: Artificial Intelligence
Knowledge-Driven Analytics
Knowledge-driven analytics is an approach to analysing data that uses existing knowledge, such as expert opinions, rules, or prior experience, to guide and interpret the analysis. This method combines data analysis with human understanding to produce more meaningful insights. It helps organisations make better decisions by considering not just raw data, but also what is…
Causal Knowledge Integration
Causal knowledge integration is the process of combining information from different sources to understand not just what is happening, but why it is happening. This involves connecting data, theories, or observations to uncover cause-and-effect relationships. By integrating causal knowledge, people and systems can make better predictions and decisions by understanding underlying mechanisms.
Uncertainty-Aware Inference
Uncertainty-aware inference is a method in machine learning and statistics where a system not only makes predictions but also estimates how confident it is in those predictions. This approach helps users understand when the system might be unsure or when the data is unclear. By quantifying uncertainty, decision-makers can be more cautious or seek additional…
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…
Graph-Based Predictive Analytics
Graph-based predictive analytics is a method that uses networks of connected data points, called graphs, to make predictions about future events or behaviours. Each data point, or node, can represent things like people, products, or places, and the connections between them, called edges, show relationships or interactions. By analysing the structure and patterns within these…
Neural Efficiency Metrics
Neural efficiency metrics are ways to measure how effectively a neural network or the human brain processes information, usually by comparing performance to the resources used. These metrics look at how much energy, computation, or activity is needed to achieve a certain level of accuracy or output. The goal is to find out if a…
Dynamic Model Scheduling
Dynamic model scheduling is a technique where computer models, such as those used in artificial intelligence or simulations, are chosen and run based on changing needs or conditions. Instead of always using the same model or schedule, the system decides which model to use and when, adapting as new information comes in. This approach helps…
Multi-Domain Inference
Multi-domain inference refers to the ability of a machine learning model to make accurate predictions or decisions across several different domains or types of data. Instead of being trained and used on just one specific kind of data or task, the model can handle varied information, such as images from different cameras, texts in different…
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.