Causal representation learning is a method in machine learning that focuses on finding the underlying cause-and-effect relationships in data. It aims to learn not just patterns or associations, but also the factors that directly influence outcomes. This helps models make better predictions and decisions by understanding what actually causes changes in the data.
Category: Embeddings & Representations
Neural Network Disentanglement
Neural network disentanglement is the process of making sure that different parts of a neural network learn to represent different features of the data, so each part is responsible for capturing a specific aspect. This helps the network learn more meaningful, separate concepts rather than mixing everything together. With disentangled representations, it becomes easier to…
Semantic Knowledge Injection
Semantic knowledge injection is the process of adding meaningful information or context to a computer system, such as a machine learning model or database, so it can understand and use that knowledge more effectively. This often involves including facts, relationships, or rules about a subject, rather than just raw data. By doing this, the system…
Contextual Embedding Alignment
Contextual embedding alignment is a process in machine learning where word or sentence representations from different sources or languages are adjusted so they can be compared or combined more effectively. These representations, called embeddings, capture the meaning of words based on their context in text. Aligning them ensures that similar meanings are close together, even…
Neural Collapse Analysis
Neural Collapse Analysis examines a surprising pattern that arises in the final stages of training deep neural networks for classification tasks. During this phase, the network’s representations for each class become highly organised: the outputs for samples from the same class cluster tightly together, and the clusters for different classes are arranged in a symmetrical,…
Convolutional Neural Filters
Convolutional neural filters are small sets of weights used in convolutional neural networks to scan input data, such as images, and detect patterns like edges or textures. They move across the input in a sliding window fashion, producing feature maps that highlight specific visual features. By stacking multiple filters and layers, the network can learn…
Dynamic Graph Representation
Dynamic graph representation is a way of modelling and storing graphs where the structure or data can change over time. This approach allows for updates such as adding or removing nodes and edges without needing to rebuild the entire graph from scratch. It is often used in situations where relationships between items are not fixed…
Dimensionality Reduction Techniques
Dimensionality reduction techniques are methods used to simplify large sets of data by reducing the number of variables or features while keeping the essential information. This helps make data easier to understand, visualise, and process, especially when dealing with complex or high-dimensional datasets. By removing less important features, these techniques can improve the performance and…
Autoencoder Architectures
Autoencoder architectures are a type of artificial neural network designed to learn efficient ways of compressing and reconstructing data. They consist of two main parts: an encoder that reduces the input data to a smaller representation, and a decoder that tries to reconstruct the original input from this smaller version. These networks are trained so…
Transferable Representations
Transferable representations are ways of encoding information so that what is learned in one context can be reused in different, but related, tasks. In machine learning, this often means creating features or patterns from data that help a model perform well on new, unseen tasks without starting from scratch. This approach saves time and resources…