Dynamic model pruning is a technique used in machine learning to make models faster and more efficient by removing unnecessary parts while the model is running, rather than before or after training. This method allows the model to adapt in real time to different tasks or resource limitations, choosing which parts to use or skip…
Adaptive learning rates are techniques used in training machine learning models where the rate at which the model learns changes automatically during the training process. Instead of using a fixed learning rate, the algorithm adjusts the rate depending on how well the model is improving. This helps the model learn more efficiently, making faster progress…
Data flow optimisation is the process of improving how data moves and is processed within a system, such as a computer program, network, or business workflow. The main goal is to reduce delays, avoid unnecessary work, and use resources efficiently. By streamlining the path that data takes, organisations can make their systems faster and more…
Quantum circuit design is the process of creating step-by-step instructions for quantum computers. It involves arranging quantum gates, which are the building blocks for manipulating quantum bits, in a specific order to perform calculations. The aim is to solve a problem or run an algorithm using the unique properties of quantum mechanics. Designing a quantum…
Federated learning scalability refers to how well a federated learning system can handle increasing numbers of participants or devices without a loss in performance or efficiency. As more devices join, the system must manage communication, computation, and data privacy across all participants. Effective scalability ensures that the learning process remains fast, accurate, and secure, even…
Inference acceleration techniques are methods used to make machine learning models, especially those used for predictions or classifications, run faster and more efficiently. These techniques reduce the time and computing power needed for a model to process new data and produce results. Common approaches include optimising software, using specialised hardware, and simplifying the model itself.
Generalisation optimisation is the process of improving how well a model or system can apply what it has learned to new, unseen situations, rather than just memorising specific examples. It focuses on creating solutions that work broadly, not just for the exact cases they were trained on. This is important in fields like machine learning,…
Domain-specific model tuning is the process of adjusting a machine learning or AI model to perform better on tasks within a particular area or industry. Instead of using a general-purpose model, the model is refined using data and examples from a specific field, such as medicine, law, or finance. This targeted tuning helps the model…
Neural Efficiency Frameworks are models or theories that focus on how brains and artificial neural networks use resources to process information in the most effective way. They look at how efficiently a neural system can solve tasks using the least energy, time or computational effort. These frameworks are used to understand both biological brains and…
Contrastive learning optimisation is a technique in machine learning where a model learns to tell apart similar and dissimilar items by comparing them in pairs or groups. The goal is to bring similar items closer together in the modelnulls understanding while pushing dissimilar items further apart. This approach helps the model create more useful and…