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,…
Category: Model Training & Tuning
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…
Stochastic Depth
Stochastic depth is a technique used in training deep neural networks, where some layers are randomly skipped during each training pass. This helps make the network more robust and reduces the risk of overfitting, as the model learns to perform well even if parts of it are not always active. By doing this, the network…
Knowledge Transferability
Knowledge transferability is the ability to apply what has been learned in one situation to a different context or problem. It means that skills, information, or methods are not limited to their original use but can help solve new challenges. This concept is important in education, technology, and the workplace, as it helps people and…
Plasma Scaling
Plasma scaling refers to adjusting the size or output of a plasma system while maintaining its performance and characteristics. This process is important for designing devices that use plasma, such as reactors or industrial machines, at different sizes for various purposes. By understanding plasma scaling, engineers can predict how changes in size or power will…
Stochastic Gradient Descent Variants
Stochastic Gradient Descent (SGD) variants are different methods built on the basic SGD algorithm, which is used to train machine learning models by updating their parameters step by step. These variants aim to improve performance by making the updates faster, more stable, or more accurate. Some common variants include Momentum, Adam, RMSprop, and Adagrad, each…
Gradient Flow Analysis
Gradient flow analysis is a method used to study how the gradients, or error signals, move through a neural network during training. This analysis helps identify if gradients are becoming too small (vanishing) or too large (exploding), which can make training difficult or unstable. By examining the gradients at different layers, researchers and engineers can…
Knowledge Injection
Knowledge injection is the process of adding specific information or facts into an artificial intelligence system, such as a chatbot or language model, to improve its accuracy or performance. This can be done by directly feeding the system extra data, rules, or context that it would not otherwise have known. Knowledge injection helps AI systems…
Label Noise Robustness
Label noise robustness refers to the ability of a machine learning model to perform well even when some of its training data labels are incorrect or misleading. In real-world datasets, mistakes can occur when humans or automated systems assign the wrong category or value to an example. Robust models can tolerate these errors and still…
Cross-Validation Techniques
Cross-validation techniques are methods used to assess how well a machine learning model will perform on information it has not seen before. By splitting the available data into several parts, or folds, these techniques help ensure that the model is not just memorising the training data but is learning patterns that generalise to new data….