Category: Model Training & Tuning

Neural Network Pruning

Neural network pruning is a technique used to reduce the size and complexity of artificial neural networks by removing unnecessary or less important connections, neurons, or layers. This process helps make models smaller and faster without significantly affecting their accuracy. Pruning often follows the training of a large model, where the least useful parts are…

Physics-Informed Neural Networks

Physics-Informed Neural Networks, or PINNs, are a type of artificial intelligence model that learns to solve problems by combining data with the underlying physical laws, such as equations from physics. Unlike traditional neural networks that rely only on data, PINNs also use mathematical rules that describe how things work in nature. This approach helps the…

Domain Randomisation

Domain randomisation is a technique used in artificial intelligence, especially in robotics and computer vision, to make models more robust. It involves exposing a model to many different simulated environments where aspects like lighting, textures, and object positions are changed randomly. By training on these varied scenarios, the model learns to perform well even when…

Policy Gradient Methods

Policy Gradient Methods are a type of approach in reinforcement learning where an agent learns to make decisions by directly improving its decision-making policy. Instead of trying to estimate the value of each action, these methods adjust the policy itself to maximise rewards over time. The agent uses feedback from its environment to gradually tweak…

Structured Prediction

Structured prediction is a type of machine learning where the goal is to predict complex outputs that have internal structure, such as sequences, trees, or grids. Unlike simple classification or regression, where each prediction is a single value or label, structured prediction models outputs that are made up of multiple related elements. This approach is…

Neural Process Models

Neural process models are computational systems that use neural networks to learn functions or processes from data. Unlike traditional neural networks that focus on mapping inputs to outputs, neural process models aim to understand entire functions, allowing them to adapt quickly to new tasks with limited data. These models are especially useful for problems where…

Neural Posterior Estimation

Neural Posterior Estimation is a machine learning technique that uses neural networks to approximate the probability of different causes or parameters given observed data. This approach is useful when traditional mathematical methods are too slow or complex to calculate these probabilities. By learning from examples, neural networks can quickly estimate how likely certain parameters are,…

Neural Tangent Generalisation

Neural Tangent Generalisation refers to understanding how large neural networks learn and make predictions by using a mathematical tool called the Neural Tangent Kernel (NTK). This approach simplifies complex neural networks by treating them like linear models when they are very wide, making their behaviour easier to analyse. Researchers use this to predict how well…