Neural architecture pruning is a technique used to make artificial neural networks smaller and faster by removing unnecessary or less important parts. This process helps reduce the size and complexity of a neural network without losing much accuracy. By carefully selecting which neurons or connections to remove, the pruned network can still perform its task…
Category: Model Optimisation Techniques
Bayesian Optimization Strategies
Bayesian optimisation strategies are methods used to efficiently find the best solution to a problem when evaluating each option is expensive or time-consuming. They work by building a model that predicts how good different options might be, then using that model to decide which option to try next. This approach helps to make the most…
Dynamic Feature Selection
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…
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…
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…
Neural Attention Scaling
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 Compression Algorithms
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…
Knowledge Fusion Techniques
Knowledge fusion techniques are methods used to combine information from different sources to create a single, more accurate or useful result. These sources may be databases, sensors, documents, or even expert opinions. The goal is to resolve conflicts, reduce errors, and fill in gaps by leveraging the strengths of each source. By effectively merging diverse…
Neural Pruning Strategies
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…
Dynamic Weight Reallocation
Dynamic Weight Reallocation is a process where the importance or weighting of different factors or components in a system is adjusted automatically over time. This adjustment is based on changing conditions, data, or feedback, allowing the system to respond to new information or priorities. It is often used in areas like machine learning, resource management,…