Category: Model Optimisation Techniques

Q-Learning Variants

Q-Learning variants are different versions or improvements of the basic Q-Learning algorithm, which is a method used in reinforcement learning to help computers learn the best actions to take in a given situation. These variants are designed to address limitations of the original algorithm, such as slow learning speed or instability. By making changes to…

Reward Shaping

Reward shaping is a technique used in reinforcement learning where additional signals are given to an agent to guide its learning process. By providing extra rewards or feedback, the agent can learn desired behaviours more quickly and efficiently. This helps the agent avoid unproductive actions and focus on strategies that lead to the main goal.

Invertible Neural Networks

Invertible neural networks are a type of artificial neural network designed so that their operations can be reversed. This means that, given the output, you can uniquely determine the input that produced it. Unlike traditional neural networks, which often lose information as data passes through layers, invertible neural networks preserve all information, making them especially…

Gas Optimization

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

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…

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…