Category: Model Training & Tuning

Dynamic Inference Paths

Dynamic inference paths refer to the ability of a system, often an artificial intelligence or machine learning model, to choose different routes or strategies for making decisions based on the specific input it receives. Instead of always following a fixed set of steps, the system adapts its reasoning process in real time to best address…

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…

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…

Decentralized AI Training

Decentralized AI training is a method where artificial intelligence models are trained across multiple computers or devices, rather than relying on a single central server. Each participant works with its own local data and shares only necessary model updates, not the data itself. This approach can improve privacy, reduce bottlenecks, and make use of distributed…

Tensor Processing Units (TPUs)

Tensor Processing Units (TPUs) are specialised computer chips designed by Google to accelerate machine learning tasks. They are optimised for handling large-scale mathematical operations, especially those involved in training and running deep learning models. TPUs are used in data centres and cloud environments to speed up artificial intelligence computations, making them much faster than traditional…