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 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 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…

Multi-task learning is a machine learning approach where a single model is trained to perform several related tasks at the same time. By learning from multiple tasks, the model can share useful information between them, often leading to better overall performance. This technique can help the model generalise better and make more efficient use of…