Category: Deep Learning

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…

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…

Heterogeneous Graph Attention

Heterogeneous graph attention is a method in machine learning that helps computers analyse and learn from complex networks containing different types of nodes and connections. Unlike standard graphs where all nodes and edges are the same, heterogeneous graphs have a mix, such as people, organisations, and products connected in various ways. The attention mechanism helps…

Graph Neural Network Scalability

Graph Neural Network scalability refers to the ability of graph-based machine learning models to efficiently process and learn from very large graphs, often containing millions or billions of nodes and edges. As graphs grow in size, memory and computation demands increase, making it challenging to train and apply these models without special techniques. Solutions for…

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…

Model-Free RL Algorithms

Model-free reinforcement learning (RL) algorithms help computers learn to make decisions by trial and error, without needing a detailed model of how their environment works. Instead of predicting future outcomes, these algorithms simply try different actions and learn from the rewards or penalties they receive. This approach is useful when it is too difficult or…