Synthetic data generation is the process of creating artificial data that mimics real-world data. This can be done using computer algorithms, which produce data that has similar patterns and properties to actual data sets. It is often used when real data is scarce, sensitive, or expensive to collect.
Category: Data Science
Quantum Machine Learning
Quantum Machine Learning combines quantum computing with machine learning techniques. It uses the special properties of quantum computers, such as superposition and entanglement, to process information in ways that are not possible with traditional computers. This approach aims to solve certain types of learning problems faster or more efficiently than classical methods. Researchers are exploring…
Data-Driven Decision Systems
Data-driven decision systems are tools or processes that help organisations make choices based on factual information and analysis, rather than intuition or guesswork. These systems collect, organise, and analyse data to uncover patterns or trends that can inform decisions. By relying on evidence from data, organisations can improve accuracy and reduce the risk of mistakes.
Data Quality Frameworks
Data quality frameworks are structured sets of guidelines and standards that organisations use to ensure their data is accurate, complete, reliable and consistent. These frameworks help define what good data looks like and set processes for measuring, maintaining and improving data quality. By following a data quality framework, organisations can make better decisions and avoid…
Differential Privacy Frameworks
Differential privacy frameworks are systems or tools that help protect individual data when analysing or sharing large datasets. They add carefully designed random noise to data or results, so that no single person’s information can be identified, even if someone tries to extract it. These frameworks allow organisations to gain useful insights from data while…
Knowledge Injection Frameworks
Knowledge injection frameworks are software tools or systems that help add external information or structured knowledge into artificial intelligence models or applications. This process improves the model’s understanding and decision-making by providing data it might not learn from its training alone. These frameworks manage how, when, and what information is inserted, ensuring consistency and relevance.
Temporal Graph Prediction
Temporal graph prediction is a technique used to forecast future changes in networks where both the structure and connections change over time. Unlike static graphs, temporal graphs capture how relationships between items or people evolve, allowing predictions about future links or behaviours. This helps in understanding and anticipating patterns in dynamic systems such as social…
Knowledge Graph Completion
Knowledge graph completion is the process of filling in missing information or relationships in a knowledge graph, which is a type of database that organises facts as connected entities. It uses techniques from machine learning and data analysis to predict and add new links or facts that were not explicitly recorded. This helps make the…
Bayesian Optimization Strategies
Bayesian optimisation strategies are methods used to efficiently find the best solution to a problem when evaluating each option is expensive or time-consuming. They work by building a model that predicts how good different options might be, then using that model to decide which option to try next. This approach helps to make the most…
Dynamic Feature Selection
Dynamic feature selection is a process in machine learning where the set of features used for making predictions can change based on the data or the situation. Unlike static feature selection, which picks a fixed set of features before training, dynamic feature selection can adapt in real time or for each prediction. This approach helps…