Neural layer optimisation is the process of adjusting the structure and parameters of the layers within a neural network to improve its performance. This can involve changing the number of layers, the number of units in each layer, or how the layers connect. The goal is to make the neural network more accurate, efficient, or…
Adversarial robustness metrics are ways to measure how well a machine learning model can withstand attempts to fool it with intentionally misleading or manipulated data. These metrics help researchers and engineers understand if their models can remain accurate when faced with small, crafted changes that might trick the model. By using these metrics, organisations can…
Quantum neural networks are a type of artificial intelligence model that combines ideas from quantum computing and traditional neural networks. They use quantum bits, or qubits, which can process information in more complex ways than normal computer bits. This allows quantum neural networks to potentially solve certain problems much faster or more efficiently than classical…
Domain-specific fine-tuning is the process of taking a general artificial intelligence model and training it further on data from a particular field or industry. This makes the model more accurate and useful for specialised tasks, such as legal document analysis or medical record summarisation. By focusing on relevant examples, the model learns the specific language,…
Neural sparsity optimisation is a technique used to make artificial neural networks more efficient by reducing the number of active connections or neurons. This process involves identifying and removing parts of the network that are not essential for accurate predictions, helping to decrease the amount of memory and computing power needed. By making neural networks…
Contrastive feature learning is a machine learning approach that helps computers learn to tell the difference between similar and dissimilar data points. The main idea is to teach a model to bring similar items closer together and push dissimilar items further apart in its understanding. This method does not rely heavily on labelled data, making…
Neural structure optimisation is the process of designing and adjusting the architecture of artificial neural networks to achieve the best possible performance for a particular task. This involves choosing how many layers and neurons the network should have, as well as how these components are connected. By carefully optimising the structure, researchers and engineers can…
Neural resilience testing is a process used to assess how well artificial neural networks can handle unexpected changes, errors or attacks. It checks if a neural network keeps working accurately when faced with unusual inputs or disruptions. This helps developers identify weaknesses and improve the reliability and safety of AI systems.
Meta-learning optimisation is a machine learning approach that focuses on teaching models how to learn more effectively. Instead of training a model for a single task, meta-learning aims to create models that can quickly adapt to new tasks with minimal data. This is achieved by optimising the learning process itself, so the model becomes better…
Weight pruning automation refers to using automated techniques to remove unnecessary or less important weights from a neural network. This process reduces the size and complexity of the model, making it faster and more efficient. Automation means that the selection of which weights to remove is handled by algorithms, requiring little manual intervention.