Neural program synthesis is a field within artificial intelligence where neural networks are trained to automatically generate computer programmes from examples or descriptions. This approach uses large datasets and deep learning models to learn how to translate tasks or specifications into executable code. The goal is to help automate or assist the process of writing…
Category: Deep Learning
End-to-End Memory Networks
End-to-End Memory Networks are a type of artificial intelligence model designed to help computers remember and use information over several steps. They combine a memory component with neural networks, allowing the model to store facts and retrieve them as needed to answer questions or solve problems. This approach is especially useful for tasks where the…
Memory Networks
Memory networks are a type of artificial intelligence model designed to help machines remember and use information over time. They combine traditional neural networks with a memory component, allowing the system to store important facts and retrieve them when needed. This helps the AI perform tasks that require recalling previous details or context, such as…
Differentiable Neural Computers
Differentiable Neural Computers (DNCs) are a type of artificial intelligence model that combines neural networks with an external memory system, allowing them to store and retrieve complex information more effectively. Unlike standard neural networks, which process information in a fixed way, DNCs can learn how to read from and write to memory, making them better…
Neural Turing Machines
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
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
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
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
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
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…