Category: Artificial Intelligence

Sparse Coding

Sparse coding is a technique used to represent data, such as images or sounds, using a small number of active components from a larger set. Instead of using every possible feature to describe something, sparse coding only uses the most important ones, making the representation more efficient. This approach helps computers process information faster and…

Knowledge-Augmented Models

Knowledge-augmented models are artificial intelligence systems that combine their own trained abilities with external sources of information, such as databases, documents or online resources. This approach helps the models provide more accurate, up-to-date and contextually relevant answers, especially when the information is too vast or changes frequently. By connecting to reliable knowledge sources, these models…

Cross-Modal Learning

Cross-modal learning is a process where information from different senses or types of data, such as images, sounds, and text, is combined to improve understanding or performance. This approach helps machines or people connect and interpret signals from various sources in a more meaningful way. By using multiple modes of data, cross-modal learning can make…

Self-Attention Mechanisms

Self-attention mechanisms are a method used in artificial intelligence to help a model focus on different parts of an input sequence when making decisions. Instead of treating each word or element as equally important, the mechanism learns which parts of the sequence are most relevant to each other. This allows for better understanding of context…

AutoML

AutoML, short for Automated Machine Learning, refers to tools and techniques that automate parts of the machine learning process. It helps users build, train, and tune machine learning models without requiring deep expertise in coding or data science. AutoML systems can handle tasks like selecting the best algorithms, optimising parameters, and evaluating model performance. This…

Gradient Boosting Machines

Gradient Boosting Machines are a type of machine learning model that combines many simple decision trees to create a more accurate and powerful prediction system. Each tree tries to correct the mistakes made by the previous ones, gradually improving the model’s performance. This method is widely used for tasks like predicting numbers or sorting items…

Neural Ordinary Differential Equations

Neural Ordinary Differential Equations (Neural ODEs) are a type of machine learning model that use the mathematics of continuous change to process information. Instead of stacking discrete layers like typical neural networks, Neural ODEs treat the transformation of data as a smooth, continuous process described by differential equations. This allows them to model complex systems…

Knowledge Tracing

Knowledge tracing is a technique used to monitor and predict a learner’s understanding of specific topics or skills over time. It uses data from quizzes, homework, and other activities to estimate how much a student knows and how likely they are to answer future questions correctly. This helps teachers and learning systems personalise instruction to…