Knowledge injection frameworks are software tools or systems that help add external information or structured knowledge into artificial intelligence models or applications. This process improves the model’s understanding and decision-making by providing data it might not learn from its training alone. These frameworks manage how, when, and what information is inserted, ensuring consistency and relevance.
Category: Artificial Intelligence
Robustness-Aware Training
Robustness-aware training is a method in machine learning that focuses on making models less sensitive to small changes or errors in input data. By deliberately exposing models to slightly altered or adversarial examples during training, the models learn to make correct predictions even when faced with unexpected or noisy data. This approach helps ensure that…
Neural Network Calibration
Neural network calibration is the process of adjusting a neural network so that its predicted probabilities accurately reflect the likelihood of an outcome. A well-calibrated model will output a confidence score that matches the true frequency of events. This is important for applications where understanding the certainty of predictions is as valuable as the predictions…
Multi-Task Learning Frameworks
Multi-Task Learning Frameworks are systems or methods that train a single machine learning model to perform several related tasks at once. By learning from multiple tasks together, the model can share useful information between them, which often leads to better results than learning each task separately. These frameworks are especially helpful when tasks are similar…
Knowledge Distillation Pipelines
Knowledge distillation pipelines are processes used to transfer knowledge from a large, complex machine learning model, known as the teacher, to a smaller, simpler model, called the student. This helps the student model learn to perform tasks almost as well as the teacher, but with less computational power and faster speeds. These pipelines involve training…
Neural Network Quantization
Neural network quantisation is a technique used to make machine learning models smaller and faster by converting their numbers from high precision (like 32-bit floating point) to lower precision (such as 8-bit integers). This process reduces the amount of memory and computing power needed to run the models, making them more efficient for use on…
Temporal Graph Prediction
Temporal graph prediction is a technique used to forecast future changes in networks where both the structure and connections change over time. Unlike static graphs, temporal graphs capture how relationships between items or people evolve, allowing predictions about future links or behaviours. This helps in understanding and anticipating patterns in dynamic systems such as social…
Graph-Based Anomaly Detection
Graph-based anomaly detection is a method used to find unusual patterns or behaviours in data that can be represented as a network or a set of connected points, called a graph. In this approach, data points are shown as nodes, and their relationships are shown as edges. By analysing how these nodes and edges connect,…
Knowledge Graph Completion
Knowledge graph completion is the process of filling in missing information or relationships in a knowledge graph, which is a type of database that organises facts as connected entities. It uses techniques from machine learning and data analysis to predict and add new links or facts that were not explicitly recorded. This helps make the…
Bayesian Optimization Strategies
Bayesian optimisation strategies are methods used to efficiently find the best solution to a problem when evaluating each option is expensive or time-consuming. They work by building a model that predicts how good different options might be, then using that model to decide which option to try next. This approach helps to make the most…