Category: Model Optimisation Techniques

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…

Adaptive Inference Models

Adaptive inference models are computer programmes that can change how they make decisions or predictions based on the situation or data they encounter. Unlike fixed models, they dynamically adjust their processing to balance speed, accuracy, or resource use. This helps them work efficiently in changing or unpredictable conditions, such as limited computing power or varying…

Sparse Model Architectures

Sparse model architectures are neural network designs where many of the connections or parameters are intentionally set to zero or removed. This approach aims to reduce the number of computations and memory required, making models faster and more efficient. Sparse models can achieve similar levels of accuracy as dense models but use fewer resources, which…

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…

Dynamic Model Scheduling

Dynamic model scheduling is a technique where computer models, such as those used in artificial intelligence or simulations, are chosen and run based on changing needs or conditions. Instead of always using the same model or schedule, the system decides which model to use and when, adapting as new information comes in. This approach helps…

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.