Category: Artificial Intelligence

Multi-Objective Learning

Multi-objective learning is a machine learning approach where a model is trained to achieve several goals at the same time, rather than just one. Instead of optimising for a single outcome, such as accuracy, the model balances multiple objectives, which may sometimes conflict with each other. This approach is useful when real-world tasks require considering…

Knowledge Transfer Networks

Knowledge Transfer Networks are organised groups or platforms that connect people, organisations, or institutions to share useful knowledge, skills, and expertise. Their main purpose is to help ideas, research, or best practices move from one place to another, so everyone benefits from new information. These networks can be formal or informal and often use meetings,…

Model Quantization Strategies

Model quantisation strategies are techniques used to reduce the size and computational requirements of machine learning models. They work by representing numbers with fewer bits, for example using 8-bit integers instead of 32-bit floating point values. This makes models run faster and use less memory, often with only a small drop in accuracy.

Temporal Feature Forecasting

Temporal feature forecasting is the process of predicting how certain characteristics or measurements change over time. It involves using historical data to estimate future values of features that vary with time, such as temperature, sales, or energy usage. This technique helps with planning and decision-making by anticipating trends and patterns before they happen.

Graph Knowledge Distillation

Graph Knowledge Distillation is a machine learning technique where a large, complex graph-based model teaches a smaller, simpler model to perform similar tasks. This process transfers important information from the big model to the smaller one, making it easier and faster to use in real situations. The smaller model learns to mimic the larger model’s…

Neural Structure Optimization

Neural structure optimisation is the process of designing and adjusting the architecture of artificial neural networks to achieve the best possible performance for a particular task. This involves choosing how many layers and neurons the network should have, as well as how these components are connected. By carefully optimising the structure, researchers and engineers can…

Bayesian Hyperparameter Tuning

Bayesian hyperparameter tuning is a method for finding the best settings for machine learning models by using probability to guide the search. Instead of trying every combination or picking values at random, it learns from previous attempts and predicts which settings are likely to work best. This makes the search more efficient and can lead…

Active Feature Sampling

Active feature sampling is a method used in machine learning to intelligently select which features, or data attributes, to use when training a model. Instead of using every available feature, the process focuses on identifying the most important ones that contribute to better predictions. This approach can help improve model accuracy and reduce computational costs…