Graph Knowledge Distillation is a machine learning technique where a large, complex graph-based model teaches a smaller, simpler model to perform similar tasks. This process transfers important information from the big model to the smaller one, making it easier and faster to use in real situations. The smaller model learns to mimic the larger model’s…
Category: Model Optimisation Techniques
Neural Structure Optimization
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…
Active Feature Sampling
Active feature sampling is a method used in machine learning to intelligently select which features, or data attributes, to use when training a model. Instead of using every available feature, the process focuses on identifying the most important ones that contribute to better predictions. This approach can help improve model accuracy and reduce computational costs…
Meta-Learning Optimization
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…
Incremental Learning Strategies
Incremental learning strategies are methods that allow a system or individual to learn new information gradually, building upon existing knowledge without needing to start over each time. This approach is common in both human learning and machine learning, where new data is incorporated step by step. Incremental learning helps in efficiently updating knowledge without forgetting…
Weight Pruning Automation
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.
Adaptive Neural Architectures
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…
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…