Knowledge Integration Networks are systems that connect information, expertise and insights from different sources to create a more complete and useful understanding. They help people or organisations bring together knowledge that might be scattered across departments, databases or even different organisations. By linking and organising this information, these networks make it easier to solve complex…
Category: Artificial Intelligence
Model Compression Pipelines
Model compression pipelines are step-by-step processes that reduce the size and complexity of machine learning models while trying to keep their performance close to the original. These pipelines often use techniques such as pruning, quantisation, and knowledge distillation to achieve smaller and faster models. The goal is to make models more suitable for devices with…
Temporal Knowledge Modeling
Temporal knowledge modelling is a way of organising information that changes over time. It helps computers and people understand not just facts, but also when those facts are true or relevant. This approach allows systems to keep track of events, sequences, and the duration of different states or relationships. For example, a person’s job history…
Anomaly Detection Optimization
Anomaly detection optimisation involves improving the methods used to find unusual patterns or outliers in data. This process focuses on making detection systems more accurate and efficient, so they can spot problems or rare events quickly and with fewer errors. Techniques might include fine-tuning algorithms, selecting better features, or adjusting thresholds to reduce false alarms…
Graph Knowledge Propagation
Graph knowledge propagation is a process where information or attributes are shared between connected nodes in a network, such as people in a social network or web pages on the internet. This sharing helps each node gain knowledge from its neighbours, allowing the system to learn or infer new relationships and properties. It is widely…
Neural Architecture Refinement
Neural architecture refinement is the process of improving the design of artificial neural networks to make them work better for specific tasks. This can involve adjusting the number of layers, changing how neurons connect, or modifying other structural features of the network. The goal is to find a structure that improves performance, efficiency, or accuracy…
Bayesian Model Optimization
Bayesian Model Optimization is a method for finding the best settings or parameters for a machine learning model by using probability to guide the search. Rather than testing every possible combination, it builds a model of which settings are likely to work well based on previous results. This approach helps to efficiently discover the most…
Active Inference Pipelines
Active inference pipelines are systems that use a process of prediction and correction to guide decision-making. They work by continuously gathering information from their environment, making predictions about what will happen next, and then updating their understanding based on what actually happens. This helps the system become better at achieving goals, as it learns from…
Neural Robustness Frameworks
Neural robustness frameworks are systems and tools designed to make artificial neural networks more reliable when facing unexpected or challenging situations. They help ensure that these networks continue to perform well even if the data they encounter is noisy, incomplete or intentionally manipulated. These frameworks often include methods for testing, defending, and improving the resilience…
Cross-Task Knowledge Transfer
Cross-Task Knowledge Transfer is when skills or knowledge learned from one task are used to improve performance on a different but related task. This approach is often used in machine learning, where a model trained on one type of data or problem can help solve another. It saves time and resources because the system does…