Category: Model Training & Tuning

Continual Learning Metrics

Continual learning metrics are methods used to measure how well a machine learning model can learn new information over time without forgetting what it has previously learned. These metrics help researchers and developers understand if a model can retain old knowledge while adapting to new tasks or data. They are essential for evaluating the effectiveness…

Neural Weight Optimization

Neural weight optimisation is the process of adjusting the values inside an artificial neural network to help it make better predictions or decisions. These values, called weights, determine how much influence each input has on the network’s output. By repeatedly testing and tweaking these weights, the network learns to perform tasks such as recognising images…

Attention Optimization Techniques

Attention optimisation techniques are methods used to help people focus better on tasks by reducing distractions and improving mental clarity. These techniques can include setting clear goals, using tools to block interruptions, and breaking work into manageable chunks. The aim is to help individuals make the most of their ability to concentrate, leading to better…

Uncertainty-Aware Inference

Uncertainty-aware inference is a method in machine learning and statistics where a system not only makes predictions but also estimates how confident it is in those predictions. This approach helps users understand when the system might be unsure or when the data is unclear. By quantifying uncertainty, decision-makers can be more cautious or seek additional…

Dynamic Model Calibration

Dynamic model calibration is the process of adjusting a mathematical or computer-based model so that its predictions match real-world data collected over time. This involves changing the model’s parameters as new information becomes available, allowing it to stay accurate in changing conditions. It is especially important for models that simulate systems where things are always…

Neural Feature Optimization

Neural feature optimisation is the process of selecting, adjusting, or engineering input features to improve the performance of neural networks. By focusing on the most important or informative features, models can learn more efficiently and make better predictions. This process can involve techniques like feature selection, transformation, or even learning new features automatically during training.

Decentralized AI Training

Decentralised AI training is a method where multiple computers or devices work together to train an artificial intelligence model, instead of relying on a single central server. Each participant shares the workload by processing data locally and then combining the results. This approach can help protect privacy, reduce costs, and make use of distributed computing…

Multi-Party Model Training

Multi-Party Model Training is a method where several independent organisations or groups work together to train a machine learning model without sharing their raw data. Each party keeps its data private but contributes to the learning process, allowing the final model to benefit from a wider range of information. This approach is especially useful when…