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: Deep Learning
Neural Module Orchestration
Neural Module Orchestration is a method in artificial intelligence where different specialised neural network components, called modules, are combined and coordinated to solve complex problems. Each module is designed for a specific task, such as recognising images, understanding text, or making decisions. By orchestrating these modules, a system can tackle tasks that are too complicated…
Domain-Invariant Representations
Domain-invariant representations are ways of encoding data so that important features remain the same, even if the data comes from different sources or environments. This helps machine learning models perform well when they encounter new data that looks different from what they were trained on. The goal is to focus on what matters for a…
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 Disentanglement Metrics
Neural disentanglement metrics are tools used to measure how well a neural network has separated different factors or features within its learned representations. These metrics help researchers understand if the network can distinguish between different aspects, such as shape and colour, in the data it processes. By evaluating disentanglement, scientists can improve models to make…
Dynamic Knowledge Tracing
Dynamic Knowledge Tracing is a method used to monitor and predict a learner’s understanding of specific topics over time. It uses data from each learning activity, such as quiz answers or homework, to estimate how well a student has mastered different skills. Unlike traditional testing, it updates its predictions as new information about the learner’s…
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…
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,…
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…