Model optimisation frameworks are software tools or libraries that help improve the efficiency, speed, and resource use of machine learning models. They provide methods to simplify or compress models, making them faster to run and easier to deploy, especially on devices with limited computing power. These frameworks often automate tasks like reducing model size, converting…
Category: Model Optimisation Techniques
Multi-Objective Optimization
Multi-objective optimisation is a process used to find solutions that balance two or more goals at the same time. Instead of looking for a single best answer, it tries to find a set of options that represent the best possible trade-offs between competing objectives. This approach is important when improving one goal makes another goal…
Model Compression Pipelines
Model compression pipelines are step-by-step processes that reduce the size and complexity of machine learning models while trying to keep their performance close to the original. These pipelines often use techniques such as pruning, quantisation, and knowledge distillation to achieve smaller and faster models. The goal is to make models more suitable for devices with…
Anomaly Detection Optimization
Anomaly detection optimisation involves improving the methods used to find unusual patterns or outliers in data. This process focuses on making detection systems more accurate and efficient, so they can spot problems or rare events quickly and with fewer errors. Techniques might include fine-tuning algorithms, selecting better features, or adjusting thresholds to reduce false alarms…
Neural Architecture Refinement
Neural architecture refinement is the process of improving the design of artificial neural networks to make them work better for specific tasks. This can involve adjusting the number of layers, changing how neurons connect, or modifying other structural features of the network. The goal is to find a structure that improves performance, efficiency, or accuracy…
Bayesian Model Optimization
Bayesian Model Optimization is a method for finding the best settings or parameters for a machine learning model by using probability to guide the search. Rather than testing every possible combination, it builds a model of which settings are likely to work well based on previous results. This approach helps to efficiently discover the most…
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…