Active learning pipelines are processes in machine learning where a model is trained by selecting the most useful data points to label and learn from, instead of using all available data. This approach helps save time and resources by focusing on examples that will most improve the model. It is especially useful when labelling data…
Neural network robustness refers to how well a neural network can maintain its accuracy and performance even when faced with unexpected or challenging inputs, such as noisy data, small errors, or deliberate attacks. A robust neural network does not easily get confused or make mistakes when the data it processes is slightly different from what…
Dynamic feature selection is a process in machine learning where the set of features used for making predictions can change based on the data or the situation. Unlike static feature selection, which picks a fixed set of features before training, dynamic feature selection can adapt in real time or for each prediction. This approach helps…
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…
Neural symbolic reasoning is an approach in artificial intelligence that combines neural networks with symbolic logic. Neural networks are good at learning from data, while symbolic logic helps with clear rules and reasoning. By joining these two methods, systems can learn from examples and also follow logical steps to solve problems or make decisions.
Model-Agnostic Meta-Learning, or MAML, is a machine learning technique designed to help models learn new tasks quickly with minimal data. Unlike traditional training, which focuses on one task, MAML prepares a model to adapt fast to many different tasks by optimising it for rapid learning. The approach works with various model types and does not…
Knowledge Transfer Protocols are structured methods or systems used to pass information, skills, or procedures from one person, group, or system to another. They help make sure that important knowledge does not get lost when people change roles, teams collaborate, or technology is updated. These protocols can be written guides, training sessions, digital tools, or…
Continual learning benchmarks are standard tests used to measure how well artificial intelligence systems can learn new tasks over time without forgetting previously learned skills. These benchmarks provide structured datasets and evaluation protocols that help researchers compare different continual learning methods. They are important for developing AI that can adapt to new information and tasks…
Neural weight sharing is a technique in artificial intelligence where different parts of a neural network use the same set of weights or parameters. This means the same learned features or filters are reused across multiple locations or layers in the network. It helps reduce the number of parameters, making the model more efficient and…
Self-adaptive neural networks are artificial intelligence systems that can automatically adjust their own structure or learning parameters as they process data. Unlike traditional neural networks that require manual tuning of architecture or settings, self-adaptive networks use algorithms to modify layers, nodes, or connections in response to the task or changing data. This adaptability helps them…