Zero Resource Learning is a method in artificial intelligence where systems learn from raw data without needing labelled examples or pre-existing resources like dictionaries. Instead of relying on human-annotated data, these systems discover patterns and structure by themselves. This approach is especially useful for languages or domains where labelled data is scarce or unavailable.

Contrastive pretraining is a method in machine learning where a model learns to tell how similar or different two pieces of data are. It does this by being shown pairs of data and trying to pull similar pairs closer together in its understanding, while pushing dissimilar pairs further apart. This helps the model build useful…

A Mixture of Experts is a machine learning model that combines several specialised smaller models, called experts, to solve complex problems. Each expert focuses on a specific part of the problem, and a gating system decides which experts to use for each input. This approach helps the overall system make better decisions by using the…

The Neural Tangent Kernel (NTK) is a mathematical tool used to study and predict how very large neural networks learn. It simplifies the behaviour of neural networks by treating them like a type of kernel method, which is a well-understood class of machine learning models. Using the NTK, researchers can analyse training dynamics and generalisation…

LoRA Fine-Tuning is a method used to adjust large pre-trained artificial intelligence models, such as language models, with less computing power and memory. Instead of changing all the model’s weights, LoRA adds small, trainable layers that adapt the model for new tasks. This approach makes it faster and cheaper to customise models for specific needs…