Robust optimisation is a method in decision-making and mathematical modelling that aims to find solutions that perform well even when there is uncertainty or variability in the input data. Instead of assuming that all information is precise, it prepares for worst-case scenarios by building in a margin of safety. This approach helps ensure that the…
Fairness-Aware Machine Learning refers to developing and using machine learning models that aim to make decisions without favouring or discriminating against individuals or groups based on sensitive characteristics such as gender, race, or age. It involves identifying and reducing biases that can exist in data or algorithms to ensure fair outcomes for everyone affected by…
Out-of-Distribution Detection is a technique used to identify when a machine learning model encounters data that is significantly different from the data it was trained on. This helps to prevent the model from making unreliable or incorrect predictions on unfamiliar inputs. Detecting these cases is important for maintaining the safety and reliability of AI systems…
Invariant Risk Minimisation is a machine learning technique designed to help models perform well across different environments or data sources. It aims to find patterns in data that stay consistent, even when conditions change. By focusing on these stable features, models become less sensitive to variations or biases present in specific datasets.
Continual learning is a method in artificial intelligence where systems are designed to keep learning and updating their knowledge over time, instead of only learning once from a fixed set of data. This approach helps machines adapt to new information or tasks without forgetting what they have already learned. It aims to make AI more…
Memory-Augmented Neural Networks are artificial intelligence systems that combine traditional neural networks with an external memory component. This memory allows the network to store and retrieve information over long periods, making it better at tasks that require remembering past events or facts. By accessing this memory, the network can solve problems that normal neural networks…
Dynamic Neural Networks are artificial intelligence models that can change their structure or operation as they process data. Unlike traditional neural networks, which have a fixed sequence of layers and operations, dynamic neural networks can adapt in real time based on the input or the task at hand. This flexibility allows them to handle a…
Neural Module Networks are a type of artificial intelligence model that break down complex problems into smaller tasks, each handled by a separate neural network module. These modules can be combined in different ways, depending on the question or task, to produce a final answer or result. This approach is especially useful for tasks like…
Knowledge amalgamation is the process of combining information, insights, or expertise from different sources to create a more complete understanding of a subject. This approach helps address gaps or inconsistencies in individual pieces of knowledge by bringing them together into a unified whole. It is often used in fields where information is spread across multiple…
Model compression is the process of making machine learning models smaller and faster without losing too much accuracy. This is done by reducing the number of parameters or simplifying the model’s structure. The goal is to make models easier to use on devices with limited memory or processing power, such as smartphones or embedded systems.