Category: Model Training & Tuning

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…

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.

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…

Feature Interaction Modeling

Feature interaction modelling is the process of identifying and understanding how different features or variables in a dataset influence each other when making predictions. Instead of looking at each feature separately, this technique examines how combinations of features work together to affect outcomes. By capturing these interactions, models can often make more accurate predictions and…

Cross-Task Generalization

Cross-task generalisation is the ability of a system, usually artificial intelligence, to apply what it has learned from one task to different but related tasks. This means a model does not need to be retrained from scratch for every new problem if the tasks share similarities. It helps create more flexible and adaptable AI that…

Knowledge Propagation Models

Knowledge propagation models describe how information, ideas, or skills spread within a group, network, or community. These models help researchers and organisations predict how quickly and widely knowledge will transfer between people. They are often used to improve learning, communication, and innovation by understanding the flow of knowledge.

Incremental Learning Strategies

Incremental learning strategies are methods that allow a system or individual to learn new information gradually, building upon existing knowledge without needing to start over each time. This approach is common in both human learning and machine learning, where new data is incorporated step by step. Incremental learning helps in efficiently updating knowledge without forgetting…

Attention Weight Optimization

Attention weight optimisation is a process used in machine learning, especially in models like transformers, to improve how a model focuses on different parts of input data. By adjusting these weights, the model learns which words or features in the input are more important for making accurate predictions. Optimising attention weights helps the model become…