Neural Turing Machines are a type of artificial intelligence model that combines a neural network with an external memory bank. This setup allows the model to read from and write to its memory, similar to how a computer program works. It is designed to help machines learn tasks that require storing and recalling information over…
Heterogeneous graph learning is a method in machine learning that works with graphs containing different types of nodes and connections. Unlike simple graphs where all nodes and edges are the same, heterogeneous graphs reflect real systems where entities and their relationships vary. This approach helps computers understand and analyse complex networks, such as social networks,…
Dynamic graph learning is a field of machine learning that focuses on analysing and understanding graphs whose structures or features change over time. Unlike static graphs, where relationships between nodes are fixed, dynamic graphs can have nodes and edges that appear, disappear, or evolve. This approach allows algorithms to model real-world situations where relationships and…
Graph Isomorphism Networks are a type of neural network designed to work with graph-structured data, such as social networks or molecules. They learn to represent nodes and their relationships by passing information along the connections in the graph. This approach helps the network recognise when two graphs have the same structure, even if the labels…
Message Passing Neural Networks (MPNNs) are a type of neural network designed to work with data structured as graphs, such as molecules or social networks. They operate by allowing nodes in a graph to exchange information with their neighbours through a series of message-passing steps. This approach helps the network learn patterns and relationships within…
Graph Convolutional Networks, or GCNs, are a type of neural network designed to work with data structured as graphs. Graphs are made up of nodes and edges, such as social networks where people are nodes and their connections are edges. GCNs help computers learn patterns and relationships in these networks, making sense of complex connections…
Neural combinatorial optimisation is a method that uses neural networks to solve complex problems where the goal is to find the best combination or arrangement from many possibilities. These problems are often difficult for traditional computers because there are too many options to check one by one. By learning from examples, neural networks can quickly…
Intrinsic motivation in reinforcement learning refers to a method where an agent is encouraged to explore and learn, not just by external rewards but also by its own curiosity or internal drives. Unlike traditional reinforcement learning, which relies mainly on rewards given for achieving specific goals, intrinsic motivation gives the agent additional signals that reward…
Soft Actor-Critic is a type of algorithm used in reinforcement learning that helps computers learn to make decisions by balancing two goals: getting rewards and staying flexible in their choices. It uses a method called maximum entropy, which means it encourages the computer to try different actions rather than always picking the same one. This…
Distributional Reinforcement Learning is a method in machine learning where an agent learns not just the average result of its actions, but the full range of possible outcomes and how likely each one is. Instead of focusing solely on expected rewards, this approach models the entire distribution of rewards the agent might receive. This allows…