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…
Category: Artificial Intelligence
Neural Symbolic Reasoning
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
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
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
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
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
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…
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…
Neural Network Modularization
Neural network modularization is a design approach where a large neural network is built from smaller, independent modules or components. Each module is responsible for a specific part of the overall task, allowing for easier development, troubleshooting, and updating. This method helps make complex networks more manageable, flexible, and reusable by letting developers swap or…
Domain Generalization Techniques
Domain generalisation techniques are methods used in machine learning to help models perform well on new, unseen data from different environments or sources. These techniques aim to make sure a model can handle differences between the data it was trained on and the data it will see in real use. This helps reduce the need…