Heterogeneous graph attention is a method in machine learning that helps computers analyse and learn from complex networks containing different types of nodes and connections. Unlike standard graphs where all nodes and edges are the same, heterogeneous graphs have a mix, such as people, organisations, and products connected in various ways. The attention mechanism helps…
Category: Deep Learning
Graph Neural Network Scalability
Graph Neural Network scalability refers to the ability of graph-based machine learning models to efficiently process and learn from very large graphs, often containing millions or billions of nodes and edges. As graphs grow in size, memory and computation demands increase, making it challenging to train and apply these models without special techniques. Solutions for…
Graph Autoencoders
Graph autoencoders are a type of machine learning model designed to work with data that can be represented as graphs, such as networks of people or connections between items. They learn to compress the information from a graph into a smaller, more manageable form, then reconstruct the original graph from this compressed version. This process…
Graph Pooling Techniques
Graph pooling techniques are methods used to reduce the size of graphs by grouping nodes or summarising information, making it easier for computers to analyse large and complex networks. These techniques help simplify the structure of a graph while keeping its essential features, which can improve the efficiency and performance of machine learning models. Pooling…
Model-Free RL Algorithms
Model-free reinforcement learning (RL) algorithms help computers learn to make decisions by trial and error, without needing a detailed model of how their environment works. Instead of predicting future outcomes, these algorithms simply try different actions and learn from the rewards or penalties they receive. This approach is useful when it is too difficult or…
Transformer Decoders
Transformer decoders are a component of the transformer neural network architecture, designed to generate sequences one step at a time. They work by taking in previously generated data and context information to predict the next item in a sequence, such as the next word in a sentence. Transformer decoders are often used in tasks that…
Self-Supervised Learning
Self-supervised learning is a type of machine learning where a system teaches itself by finding patterns in unlabelled data. Instead of relying on humans to label the data, the system creates its own tasks and learns from them. This approach allows computers to make use of large amounts of raw data, which are often easier…
Catastrophic Forgetting
Catastrophic forgetting is a problem in machine learning where a model trained on new data quickly loses its ability to recall or perform well on tasks it previously learned. This happens most often when a neural network is trained on one task, then retrained on a different task without access to the original data. As…
Curriculum Learning
Curriculum Learning is a method in machine learning where a model is trained on easier examples first, then gradually introduced to more difficult ones. This approach is inspired by how humans often learn, starting with basic concepts before moving on to more complex ideas. The goal is to help the model learn more effectively and…
AI for Drug Discovery
AI for Drug Discovery refers to the use of artificial intelligence techniques to help identify and develop new medicines. These systems can analyse large amounts of scientific data much faster than humans, finding patterns and connections that might otherwise be missed. By using AI, researchers can predict how different chemical compounds might affect the body,…