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…
Category: Artificial Intelligence
Adversarial Robustness
Behavioral Biometrics
Behavioural biometrics is a technology that identifies or verifies people based on how they interact with devices or systems. It analyses patterns such as typing speed, mouse movements, touchscreen gestures, or how someone walks. These patterns are unique to individuals and can be used to strengthen security or personalise user experiences. Unlike passwords or fingerprints,…
Stochastic Gradient Descent Variants
Stochastic Gradient Descent (SGD) variants are different methods built on the basic SGD algorithm, which is used to train machine learning models by updating their parameters step by step. These variants aim to improve performance by making the updates faster, more stable, or more accurate. Some common variants include Momentum, Adam, RMSprop, and Adagrad, each…
Model Interpretability
Model interpretability refers to how easily a human can understand the decisions or predictions made by a machine learning model. It is about making the inner workings of a model transparent, so people can see why it made a certain choice. This is important for trust, accountability, and identifying mistakes or biases in automated systems.
Knowledge Injection
Knowledge injection is the process of adding specific information or facts into an artificial intelligence system, such as a chatbot or language model, to improve its accuracy or performance. This can be done by directly feeding the system extra data, rules, or context that it would not otherwise have known. Knowledge injection helps AI systems…
Synthetic Feature Generation
Synthetic feature generation is the process of creating new data features from existing ones to help improve the performance of machine learning models. These new features are not collected directly but are derived by combining, transforming, or otherwise manipulating the original data. This helps models find patterns that may not be obvious in the raw…
Label Noise Robustness
Label noise robustness refers to the ability of a machine learning model to perform well even when some of its training data labels are incorrect or misleading. In real-world datasets, mistakes can occur when humans or automated systems assign the wrong category or value to an example. Robust models can tolerate these errors and still…
Multi-Task Learning
Multi-task learning is a machine learning approach where a single model is trained to perform several related tasks at the same time. By learning from multiple tasks, the model can share useful information between them, often leading to better overall performance. This technique can help the model generalise better and make more efficient use of…
Neural Symbolic Integration
Neural Symbolic Integration is an approach in artificial intelligence that combines neural networks, which learn from data, with symbolic reasoning systems, which follow logical rules. This integration aims to create systems that can both recognise patterns and reason about them, making decisions based on both learned experience and clear, structured logic. The goal is to…