Category: Model Optimisation Techniques

Neural Network Compression

Neural network compression refers to techniques used to make large artificial neural networks smaller and more efficient without significantly reducing their performance. This process helps reduce the memory, storage, and computing power required to run these models. By compressing neural networks, it becomes possible to use them on devices with limited resources, such as smartphones…

Efficient Attention Mechanisms

Efficient attention mechanisms are methods used in artificial intelligence to make the attention process faster and use less computer memory. Traditional attention methods can become slow or require too much memory when handling long sequences of data, such as long texts or audio. Efficient attention techniques solve this by simplifying calculations or using clever tricks,…

Weight Sharing Techniques

Weight sharing techniques are methods used in machine learning models where the same set of parameters, or weights, is reused across different parts of the model. This approach reduces the total number of parameters, making models smaller and more efficient. Weight sharing is especially common in convolutional neural networks and models designed for tasks like…

Model Distillation Frameworks

Model distillation frameworks are tools or libraries that help make large, complex machine learning models smaller and more efficient by transferring their knowledge to simpler models. This process keeps much of the original model’s accuracy while reducing the size and computational needs. These frameworks automate and simplify the steps needed to train, evaluate, and deploy…

Inference Latency Reduction

Inference latency reduction refers to techniques and strategies used to decrease the time it takes for a computer model, such as artificial intelligence or machine learning systems, to produce results after receiving input. This is important because lower latency means faster responses, which is especially valuable in applications where real-time or near-instant feedback is needed….

Neural Network Quantization

Neural network quantisation is a technique that reduces the amount of memory and computing power needed by a neural network. It works by representing the numbers used in the network, such as weights and activations, with lower-precision values instead of the usual 32-bit floating-point numbers. This makes the neural network smaller and faster, while often…

Knowledge Sparsification

Knowledge sparsification is the process of reducing the amount of information or connections in a knowledge system while keeping its most important parts. This helps make large and complex knowledge bases easier to manage and use. By removing redundant or less useful data, knowledge sparsification improves efficiency and can make machine learning models faster and…

Graph Pooling Techniques

Graph pooling techniques are methods used to reduce the size of graphs by grouping nodes or summarising information, making it easier for computers to analyse large and complex networks. These techniques help simplify the structure of a graph while keeping its essential features, which can improve the efficiency and performance of machine learning models. Pooling…

Value Function Approximation

Value function approximation is a technique in machine learning and reinforcement learning where a mathematical function is used to estimate the value of being in a particular situation or state. Instead of storing a value for every possible situation, which can be impractical in large or complex environments, an approximation uses a formula or model…

Policy Iteration Techniques

Policy iteration techniques are methods used in reinforcement learning to find the best way for an agent to make decisions in a given environment. The process involves two main steps: evaluating how good a current plan or policy is, and then improving it based on what has been learned. By repeating these steps, the technique…