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…
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 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…
Neural attention scaling refers to the methods and techniques used to make attention mechanisms in neural networks work efficiently with very large datasets or models. As models grow in size and complexity, calculating attention for every part of the data can become extremely demanding. Scaling solutions aim to reduce the computational resources needed, either by…
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…
Neural compression algorithms use artificial neural networks to reduce the size of digital data such as images, audio, or video. They learn to find patterns and redundancies in the data, allowing them to represent the original content with fewer bits while keeping quality as high as possible. These algorithms are often more efficient than traditional…
Neural pruning strategies refer to methods used to remove unnecessary or less important parts of a neural network, such as certain connections or neurons. The goal is to make the network smaller and faster without significantly reducing its accuracy. This helps in saving computational resources and can make it easier to run models on devices…
Adversarial example defence refers to techniques and methods used to protect machine learning models from being tricked by deliberately altered inputs. These altered inputs, called adversarial examples, are designed to look normal to humans but cause the model to make mistakes. Defences help ensure the model remains accurate and reliable even when faced with such…
Neural network interpretability is the process of understanding and explaining how a neural network makes its decisions. Since neural networks often function as complex black boxes, interpretability techniques help people see which inputs influence the output and why certain predictions are made. This makes it easier for users to trust and debug artificial intelligence systems,…