Category: Embeddings & Representations

Temporal Graph Embedding

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Embeddings & Representations, Graph-Based Learning

Temporal graph embedding is a method for converting nodes and connections in a dynamic network into numerical vectors that capture how the network changes over time. These embeddings help computers understand and analyse evolving relationships, such as friendships or transactions, as they appear and disappear. By using temporal graph embedding, it becomes easier to predict…

Graph Embedding Propagation

Post author By EfficiencyAI
Post date 1 June 2025
Categories In Artificial Intelligence, Embeddings & Representations, Graph-Based Learning

Graph embedding propagation is a technique used to represent nodes, edges, or entire graphs as numerical vectors while sharing information between connected nodes. This process allows the relationships and structural information of a graph to be captured in a format suitable for machine learning tasks. By propagating information through the graph, each node’s representation is…

Robust Feature Learning

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Data Science, Embeddings & Representations

Robust feature learning is a process in machine learning where models are trained to identify and use important patterns or characteristics in data, even when the data is noisy or contains errors. This means the features the model relies on will still work well if the data changes slightly or if there are unexpected variations….

Feature Disentanglement

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Embeddings & Representations, Explainability & Interpretability

Feature disentanglement is a process in machine learning where a model learns to separate different underlying factors or features within complex data. By doing this, the model can better understand and represent the data, making it easier to interpret or manipulate. This approach helps prevent the mixing of unrelated features, so each important aspect of…

Memory Networks

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

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…

Knowledge Graph Embeddings

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Embeddings & Representations, Graph-Based Learning

Knowledge graph embeddings are a way to represent the information from a knowledge graph as numbers that computers can easily work with. In a knowledge graph, data is organised as entities and relationships, like a network of connected facts. Embeddings translate these complex connections into vectors, which are lists of numbers, so machine learning models…

Neural Fields

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

Neural fields are a way to use neural networks to represent and process continuous data, like shapes or scenes, as mathematical functions. Instead of storing every detail as a list of values, neural fields learn to generate the values for any point in space by using a network. This approach can store complex information efficiently…

Geometric Deep Learning

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Deep Learning, Embeddings & Representations, Graph-Based Learning

Geometric deep learning is a field of machine learning that focuses on using shapes, graphs, and other complex structures as data instead of just fixed grids like images or text. It allows computers to analyse and learn from data that has relationships or connections, such as social networks, molecules, or 3D shapes. This approach helps…

Equivariant Neural Networks

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

Equivariant neural networks are a type of artificial neural network designed so that their outputs change predictably when the inputs are transformed. For example, if you rotate or flip an image, the network’s response changes in a consistent way that matches the transformation. This approach helps the network recognise patterns or features regardless of their…

Implicit Neural Representations

Post author By EfficiencyAI
Post date 31 May 2025
Categories In Artificial Intelligence, Deep Learning, Embeddings & Representations

Implicit neural representations are a way of storing information like images, 3D shapes or sound using neural networks. Instead of saving data as a grid of numbers or pixels, the neural network learns a mathematical function that can produce any part of the data when asked. This makes it possible to store complex data in…