Model inference optimisation is the process of making machine learning models run faster and more efficiently when they are used to make predictions. This involves improving the way models use computer resources, such as memory and processing power, without changing the results they produce. Techniques may include simplifying the model, using better hardware, or modifying…
Category: Model Optimisation Techniques
Neural Feature Optimization
Neural feature optimisation is the process of selecting and adjusting the most useful characteristics, or features, that a neural network uses to make decisions. This process aims to improve the performance and accuracy of neural networks by focusing on the most relevant information and reducing noise or irrelevant data. Effective feature optimisation can lead to…
Neural Activation Optimization
Neural Activation Optimization is a process in artificial intelligence where the patterns of activity in a neural network are adjusted to improve performance or achieve specific goals. This involves tweaking how the artificial neurons respond to inputs, helping the network learn better or produce more accurate outputs. It can be used to make models more…
Model Inference Metrics
Model inference metrics are measurements used to evaluate how well a machine learning model performs when making predictions on new data. These metrics help determine if the model is accurate, fast, and reliable enough for practical use. Common metrics include accuracy, precision, recall, latency, and throughput, each offering insight into different aspects of the model’s…
Quantum Error Analysis
Quantum error analysis is the study of how mistakes, or errors, affect the calculations in a quantum computer. Because quantum bits are very sensitive, they can be disturbed easily by their surroundings, causing problems in the results. Analysing these errors helps researchers understand where mistakes come from and how often they happen, so they can…
Model Calibration Frameworks
Model calibration frameworks are systems or sets of methods used to adjust the predictions of a mathematical or machine learning model so that they better match real-world outcomes. Calibration helps ensure that when a model predicts a certain probability, that probability is accurate and reliable. This process is important for making trustworthy decisions based on…
Quantum Data Efficiency
Quantum data efficiency refers to how effectively quantum computers use data during calculations. It focuses on minimising the amount of data and resources needed to achieve accurate results. This is important because quantum systems are sensitive and often have limited capacity, so making the best use of data helps improve performance and reduce errors. Efficient…
AI-Driven Optimization
AI-driven optimisation uses artificial intelligence to make processes, systems or decisions work better by analysing data and finding the most effective solutions. It often involves machine learning algorithms that can learn from past outcomes and improve over time. This approach saves time, reduces costs and helps achieve better results in complex situations where there are…
Quantum Error Calibration
Quantum error calibration is the process of identifying, measuring, and adjusting for errors that can occur in quantum computers. Because quantum bits, or qubits, are extremely sensitive to their environment, they can easily be disturbed and give incorrect results. Calibration helps to keep the system running accurately by fine-tuning the hardware and software so that…
Quantum Model Optimization
Quantum model optimisation is the process of improving the performance of quantum algorithms or machine learning models that run on quantum computers. It involves adjusting parameters or structures to achieve better accuracy, speed, or resource efficiency. This is similar to tuning traditional models, but it must account for the unique behaviours and limitations of quantum…