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…
Category: Artificial Intelligence
Knowledge Tracing
Knowledge tracing is a technique used to monitor and predict a learner’s understanding of specific topics or skills over time. It uses data from quizzes, homework, and other activities to estimate how much a student knows and how likely they are to answer future questions correctly. This helps teachers and learning systems personalise instruction to…
Spectral Clustering
Spectral clustering is a method used to group data points into clusters based on how closely they are connected to each other. It works by representing the data as a graph, where each point is a node and edges show how similar points are. The technique uses mathematics from linear algebra, specifically eigenvalues and eigenvectors,…
Graph Attention Networks
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…
Energy-Based Models
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
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…
Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) is a type of algorithm used in reinforcement learning to train agents to make good decisions. PPO improves how agents learn by making small, safe updates to their behaviour, which helps prevent them from making drastic changes that could reduce their performance. It is popular because it is relatively easy to…
Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) is a computer algorithm used to make decisions, especially in games or situations where there are many possible moves and outcomes. It works by simulating many random possible futures from the current situation, then using the results to decide which move gives the best chance of success. MCTS gradually builds…
Temporal Difference Learning
Temporal Difference Learning is a method used in machine learning where an agent learns how to make decisions by gradually improving its predictions based on feedback from its environment. It combines ideas from dynamic programming and Monte Carlo methods, allowing learning from incomplete sequences of events. This approach helps the agent adjust its understanding over…
Neural Radiance Fields (NeRF)
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…