Adaptive neural architectures are artificial intelligence systems designed to change their structure or behaviour based on the task or data they encounter. Unlike traditional neural networks that have a fixed design, these systems can adjust aspects such as the number of layers, types of connections, or processing strategies while learning or during operation. This flexibility…
Category: Model Optimisation Techniques
Sparse Feature Extraction
Sparse feature extraction is a technique in data analysis and machine learning that focuses on identifying and using only the most important or relevant pieces of information from a larger set of features. Rather than working with every possible detail, it selects a smaller number of features that best represent the data. This approach helps…
Attention Weight Optimization
Attention weight optimisation is a process used in machine learning, especially in models like transformers, to improve how a model focuses on different parts of input data. By adjusting these weights, the model learns which words or features in the input are more important for making accurate predictions. Optimising attention weights helps the model become…
Neural Memory Optimization
Neural memory optimisation refers to methods used to improve how artificial neural networks store and recall information. By making memory processes more efficient, these networks can learn faster and handle larger or more complex data. Techniques include streamlining the way information is saved, reducing unnecessary memory use, and finding better ways to retrieve stored knowledge…
Dynamic Inference Scheduling
Dynamic inference scheduling is a technique used in artificial intelligence and machine learning systems to decide when and how to run model predictions, based on changing conditions or resource availability. Instead of running all predictions at fixed times or in a set order, the system adapts its schedule to optimise performance, reduce delays, or save…
Neural Activation Sparsity
Neural activation sparsity refers to the idea that, within a neural network, only a small number of neurons are active or produce significant outputs for a given input. This means that most neurons remain inactive or have very low activity at any one time. Sparsity can help make neural networks more efficient and can improve…
Adaptive Layer Scaling
Adaptive Layer Scaling is a technique used in machine learning models, especially deep neural networks, to automatically adjust the influence or scale of each layer during training. This helps the model allocate more attention to layers that are most helpful for the task and reduce the impact of less useful layers. By dynamically scaling layers,…
Feature Space Regularization
Feature space regularisation is a method used in machine learning to prevent models from overfitting by adding constraints to how features are represented within the model. It aims to control the complexity of the learnt feature representations, ensuring that the model does not rely too heavily on specific patterns in the training data. By doing…
Neural Gradient Harmonization
Neural Gradient Harmonisation is a technique used in training neural networks to balance how the model learns from different types of data. It adjusts the way the network updates its internal parameters, especially when some data points are much easier or harder for the model to learn from. By harmonising the gradients, it helps prevent…
AI Hardware Acceleration
AI hardware acceleration refers to the use of specialised computer chips or devices designed to make artificial intelligence tasks faster and more efficient. Instead of relying only on general-purpose processors, such as CPUs, hardware accelerators like GPUs, TPUs, or FPGAs handle complex calculations required for AI models. These accelerators can process large amounts of data…