Category: Artificial Intelligence

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…

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…

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.

Adversarial Robustness Metrics

Adversarial robustness metrics are ways to measure how well a machine learning model can withstand attempts to fool it with intentionally misleading or manipulated data. These metrics help researchers and engineers understand if their models can remain accurate when faced with small, crafted changes that might trick the model. By using these metrics, organisations can…

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…