Semantic entropy regularisation is a technique used in machine learning to encourage models to make more confident and meaningful predictions. By adjusting how uncertain a model is about its outputs, it helps the model avoid being too indecisive or too certain without reason. This can improve the quality and reliability of the model’s results, especially…
Category: Model Optimisation Techniques
Prompt-Latent Caching
Prompt-Latent Caching is a technique used in artificial intelligence and machine learning systems to save the results of processed prompts, or their intermediate representations, so they do not need to be recalculated each time. By storing these results, systems can respond faster to repeated or similar requests, reducing computational costs and time. This method is…
Sparse Decoder Design
Sparse decoder design refers to creating decoder systems, often in artificial intelligence or communications, where only a small number of connections or pathways are used at any one time. This approach helps reduce complexity and resource use by focusing only on the most important or relevant features. Sparse decoders can improve efficiency and speed while…
Cross-Model Memory Sharing
Cross-Model Memory Sharing is a technique that allows different machine learning models or artificial intelligence systems to access and use the same memory or data storage. This means that information learned or stored by one model can be directly used by another without duplication. It helps models work together more efficiently, saving resources and improving…
Adaptive Context Windows
Adaptive context windows refer to the ability of an AI system or language model to change the amount of information it considers at one time based on the task or conversation. Instead of always using a fixed number of words or sentences, the system can dynamically adjust how much context it looks at to improve…
ML Optimisation Agent
An ML Optimisation Agent is a computer program or system that automatically improves the performance of machine learning models. It uses feedback and data to adjust the model’s parameters, settings, or strategies, aiming to make predictions more accurate or efficient. These agents can work by trying different approaches and learning from results, so they can…
Echo Suppression
Echo suppression is a technique used in audio and telecommunication systems to reduce or eliminate echoes that can occur during a conversation. Echoes happen when a speaker’s voice is picked up by their own microphone after bouncing back from the other person’s device or the environment. By detecting and minimising these unwanted sounds, echo suppression…
Regression Sets
Regression sets are collections of test cases used to check that recent changes in software have not caused any existing features or functions to stop working as expected. They help ensure that updates, bug fixes, or new features do not introduce new errors into previously working areas. These sets are usually run automatically and are…
Accuracy Drops
Accuracy drops refer to a noticeable decrease in how well a system or model makes correct predictions or outputs. This can happen suddenly or gradually, and often signals that something has changed in the data, environment, or the way the system is being used. Identifying and understanding accuracy drops is important for maintaining reliable performance…
Model Benchmarks
Model benchmarks are standard tests or sets of tasks used to measure and compare the performance of different machine learning models. These benchmarks provide a common ground for evaluating how well models handle specific challenges, such as recognising images, understanding language, or making predictions. By using the same tests, researchers and developers can objectively assess…