Model compression is the process of making machine learning models smaller and faster without losing too much accuracy. This is done by reducing the number of parameters or simplifying the model’s structure. The goal is to make models easier to use on devices with limited memory or processing power, such as smartphones or embedded systems.
Teacher-Student Models are a technique in machine learning where a larger, more powerful model (the teacher) is used to train a smaller, simpler model (the student). The teacher model first learns a task using lots of data and computational resources. Then, the student model learns by imitating the teacher, allowing it to achieve similar performance…
Cross-modal learning is a process where information from different senses or types of data, such as images, sounds, and text, is combined to improve understanding or performance. This approach helps machines or people connect and interpret signals from various sources in a more meaningful way. By using multiple modes of data, cross-modal learning can make…
Self-attention mechanisms are a method used in artificial intelligence to help a model focus on different parts of an input sequence when making decisions. Instead of treating each word or element as equally important, the mechanism learns which parts of the sequence are most relevant to each other. This allows for better understanding of context…
Neural Style Transfer is a technique in artificial intelligence that blends the artistic style of one image with the content of another. It uses deep learning to analyse and separate the elements that make up the ‘style’ and ‘content’ of images. The result is a new image that looks like the original photo painted in…
Neural Ordinary Differential Equations (Neural ODEs) are a type of machine learning model that use the mathematics of continuous change to process information. Instead of stacking discrete layers like typical neural networks, Neural ODEs treat the transformation of data as a smooth, continuous process described by differential equations. This allows them to model complex systems…
Graph Attention Networks, or GATs, are a type of neural network designed to work with data structured as graphs. Unlike traditional neural networks that process fixed-size data like images or text, GATs can handle nodes and their connections directly. They use an attention mechanism to decide which neighbouring nodes are most important when making predictions…
Normalising flows are mathematical methods used to transform simple probability distributions into more complex ones. They do this by applying a series of reversible steps, making it possible to model complicated data patterns while still being able to calculate probabilities exactly. This approach is especially useful in machine learning for tasks that require both flexible…
Energy-Based Models are a type of machine learning model that use an energy function to measure how well a set of variables fits a particular configuration. The model assigns lower energy to more likely or desirable configurations and higher energy to less likely ones. By finding the configurations that minimise the energy, the model can…
Actor-Critic Methods are a group of algorithms used in reinforcement learning where two components work together to help an agent learn. The actor decides which actions to take, while the critic evaluates how good those actions are based on the current situation. This collaboration allows the agent to improve its decision-making over time by using…