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…
Gas optimisation refers to the practice of reducing the amount of computational resources, known as gas, needed to execute transactions or smart contracts on blockchain platforms such as Ethereum. By optimising code and minimising unnecessary operations, developers can make transactions more efficient and less expensive. Gas optimisation is important because high gas usage can lead…
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 (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 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…
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 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….
Robust optimisation is a method in decision-making and mathematical modelling that aims to find solutions that perform well even when there is uncertainty or variability in the input data. Instead of assuming that all information is precise, it prepares for worst-case scenarios by building in a margin of safety. This approach helps ensure that the…
Invariant Risk Minimisation is a machine learning technique designed to help models perform well across different environments or data sources. It aims to find patterns in data that stay consistent, even when conditions change. By focusing on these stable features, models become less sensitive to variations or biases present in specific datasets.
Pruning-aware training is a machine learning technique where a model is trained with the knowledge that parts of it will be removed, or pruned, later. This helps the model maintain good performance even after some connections or neurons are taken out to make it smaller or faster. By planning for pruning during training, the final…