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…
Neural Radiance Fields, or NeRF, is a method in computer graphics that uses artificial intelligence to create detailed 3D scenes from a collection of 2D photographs. It works by learning how light behaves at every point in a scene, allowing it to predict what the scene looks like from any viewpoint. This technique makes it…
Meta-learning is a method in machine learning where algorithms are designed to learn how to learn. Instead of focusing on solving a single task, meta-learning systems aim to improve their ability to adapt to new tasks quickly by using prior experience. This approach helps machines become more flexible, allowing them to handle new problems with…
Bayesian Neural Networks are a type of artificial neural network that use probability to handle uncertainty in their predictions. Instead of having fixed values for their weights, they represent these weights as probability distributions. This approach helps the model estimate not just an answer, but also how confident it is in that answer, which can…
Variational Autoencoders, or VAEs, are a type of machine learning model that learns to compress data, like images or text, into a simpler form and then reconstructs it back to the original format. They are designed to not only recreate the data but also understand its underlying patterns. VAEs use probability to make their compressed…