Domain-invariant representations are ways of encoding data so that important features remain the same, even if the data comes from different sources or environments. This helps machine learning models perform well when they encounter new data that looks different from what they were trained on. The goal is to focus on what matters for a…
Category: Embeddings & Representations
Neural Disentanglement Metrics
Neural disentanglement metrics are tools used to measure how well a neural network has separated different factors or features within its learned representations. These metrics help researchers understand if the network can distinguish between different aspects, such as shape and colour, in the data it processes. By evaluating disentanglement, scientists can improve models to make…
Graph Embedding Propagation
Graph embedding propagation is a technique used to represent nodes, edges, or entire graphs as vectors of numbers, while spreading information across the graph structure. This process allows the properties and relationships of nodes to influence each other, so that the final vector captures both the characteristics of a node and its position in the…
Knowledge Encoding Strategies
Knowledge encoding strategies are methods used to organise and store information so it can be remembered and retrieved later. These strategies help people and machines make sense of new knowledge by turning it into formats that are easier to understand and recall. Good encoding strategies can improve learning, memory, and problem-solving by making information more…
Cross-Modal Alignment
Cross-modal alignment refers to the process of connecting information from different types of data, such as images, text, or sound, so that they can be understood and used together by computer systems. This allows computers to find relationships between, for example, a picture and a description, or a spoken word and a written sentence. It…
Semantic Drift Compensation
Semantic drift compensation is the process of adjusting for changes in the meaning of words or phrases over time or across different contexts. As language evolves, the same term can develop new meanings or lose old ones, which can cause confusion in language models, search engines, or translation systems. Semantic drift compensation uses algorithms or…
Feature Space Regularization
Feature space regularisation is a method used in machine learning to prevent models from overfitting by adding constraints to how features are represented within the model. It aims to control the complexity of the learnt feature representations, ensuring that the model does not rely too heavily on specific patterns in the training data. By doing…
Contrastive Representation Learning
Contrastive representation learning is a machine learning technique that helps computers learn useful features from data by comparing examples. The main idea is to bring similar items closer together and push dissimilar items further apart in the learned representation space. This approach is especially useful when there are few or no labels for the data,…
Cross-Domain Transferability
Cross-domain transferability refers to the ability of a model, skill, or system to apply knowledge or solutions learned in one area to a different, often unrelated, area. This concept is important in artificial intelligence and machine learning, where a model trained on one type of data or task is expected to perform well on another…
Sparse Neural Representations
Sparse neural representations refer to a way of organising information in neural networks so that only a small number of neurons are active or used at any one time. This approach mimics how the human brain often works, where only a few cells respond to specific stimuli, making the system more efficient. Sparse representations can…