Multi-modal data fusion is the process of combining information from different types of data sources, such as images, text, audio, or sensor readings, to gain a more complete understanding of a situation or problem. By integrating these diverse data types, systems can make better decisions and provide more accurate results than using a single source…
Knowledge fusion techniques are methods used to combine information from different sources to create a single, more accurate or useful result. These sources may be databases, sensors, documents, or even expert opinions. The goal is to resolve conflicts, reduce errors, and fill in gaps by leveraging the strengths of each source. By effectively merging diverse…
Neural pruning strategies refer to methods used to remove unnecessary or less important parts of a neural network, such as certain connections or neurons. The goal is to make the network smaller and faster without significantly reducing its accuracy. This helps in saving computational resources and can make it easier to run models on devices…
Contextual embedding alignment is a process in machine learning where word or sentence representations from different sources or languages are adjusted so they can be compared or combined more effectively. These representations, called embeddings, capture the meaning of words based on their context in text. Aligning them ensures that similar meanings are close together, even…
Adversarial example defence refers to techniques and methods used to protect machine learning models from being tricked by deliberately altered inputs. These altered inputs, called adversarial examples, are designed to look normal to humans but cause the model to make mistakes. Defences help ensure the model remains accurate and reliable even when faced with such…
Neural network interpretability is the process of understanding and explaining how a neural network makes its decisions. Since neural networks often function as complex black boxes, interpretability techniques help people see which inputs influence the output and why certain predictions are made. This makes it easier for users to trust and debug artificial intelligence systems,…
Dynamic Weight Reallocation is a process where the importance or weighting of different factors or components in a system is adjusted automatically over time. This adjustment is based on changing conditions, data, or feedback, allowing the system to respond to new information or priorities. It is often used in areas like machine learning, resource management,…
Neural Collapse Analysis examines a surprising pattern that arises in the final stages of training deep neural networks for classification tasks. During this phase, the network’s representations for each class become highly organised: the outputs for samples from the same class cluster tightly together, and the clusters for different classes are arranged in a symmetrical,…
A Task Automation System is a software tool or platform designed to perform repetitive tasks automatically, without the need for manual intervention. It helps users save time and reduce errors by handling routine processes, such as sending emails, generating reports, or managing data entries. These systems can be customised to fit different needs and are…
Time tracking automation uses technology to automatically monitor and record how time is spent on tasks or projects, reducing the need for manual input. It helps individuals and teams understand where their time goes by capturing activity data from devices or software. This process makes time management more accurate and efficient, which can support better…