Category: Model Optimisation Techniques

Graph Neural Network Pruning

Graph neural network pruning is a technique used to make graph neural networks (GNNs) smaller and faster by removing unnecessary parts of the model. These parts can include nodes, edges, or parameters that do not contribute much to the final prediction. Pruning helps reduce memory use and computation time while keeping most of the model’s…

Multi-Objective Reinforcement Learning

Multi-Objective Reinforcement Learning is a type of machine learning where an agent learns to make decisions by balancing several goals at the same time. Instead of optimising a single reward, the agent considers multiple objectives, which can sometimes conflict with each other. This approach helps create solutions that are better suited to real-life situations where…

Reward Sparsity Handling

Reward sparsity handling refers to techniques used in machine learning, especially reinforcement learning, to address situations where positive feedback or rewards are infrequent or delayed. When an agent rarely receives rewards, it can struggle to learn which actions are effective. By using special strategies, such as shaping rewards or providing hints, learning can be made…

Policy Gradient Optimization

Policy Gradient Optimisation is a method used in machine learning, especially in reinforcement learning, to help an agent learn the best actions to take to achieve its goals. Instead of trying out every possible action, the agent improves its decision-making by gradually changing its strategy based on feedback from its environment. This approach directly adjusts…

Sample-Efficient Reinforcement Learning

Sample-efficient reinforcement learning is a branch of artificial intelligence that focuses on training systems to learn effective behaviours from as few interactions or data samples as possible. This approach aims to reduce the amount of experience or data needed for an agent to perform well, making it practical for real-world situations where gathering data is…

Inference Pipeline Optimization

Inference pipeline optimisation is the process of making the steps that turn machine learning models into predictions faster and more efficient. It involves improving how data is prepared, how models are run, and how results are delivered. The goal is to reduce waiting time and resource usage while keeping results accurate and reliable.

Dimensionality Reduction Techniques

Dimensionality reduction techniques are methods used to simplify large sets of data by reducing the number of variables or features while keeping the essential information. This helps make data easier to understand, visualise, and process, especially when dealing with complex or high-dimensional datasets. By removing less important features, these techniques can improve the performance and…

Feature Selection Algorithms

Feature selection algorithms are techniques used in data analysis to pick out the most important pieces of information from a large set of data. These algorithms help identify which inputs, or features, are most useful for making accurate predictions or decisions. By removing unnecessary or less important features, these methods can make models faster, simpler,…