Category: Artificial Intelligence

Encrypted Model Inference

Encrypted model inference is a method that allows machine learning models to make predictions on data without ever seeing the raw, unencrypted information. This is achieved by using special cryptographic techniques so that the data remains secure and private throughout the process. The model processes encrypted data and produces encrypted results, which can then be…

Federated Differential Privacy

Federated Differential Privacy is a method that combines federated learning and differential privacy to protect individual data during collaborative machine learning. In federated learning, many users train a shared model without sending their raw data to a central server. Differential privacy adds mathematical noise to the updates or results, making it very hard to identify…

Knowledge Graph Completion

Knowledge graph completion is the process of filling in missing information or relationships within a knowledge graph. A knowledge graph is a structured network of facts, where entities like people, places, or things are connected by relationships. Because real-world data is often incomplete, algorithms are used to predict and add missing links or facts, making…

Dynamic Graph Representation

Dynamic graph representation is a way of modelling and storing graphs where the structure or data can change over time. This approach allows for updates such as adding or removing nodes and edges without needing to rebuild the entire graph from scratch. It is often used in situations where relationships between items are not fixed…

Graph-Based Anomaly Detection

Graph-based anomaly detection is a technique used to find unusual patterns or outliers in data that can be represented as networks or graphs, such as social networks or computer networks. It works by analysing the structure and connections between nodes to spot behaviours or patterns that do not fit the general trend. This method is…

Graph Signal Processing

Graph Signal Processing (GSP) is a field that studies how to analyse and process data that lives on graphs, such as social networks or transportation systems. It extends traditional signal processing, which deals with time or space signals, to more complex structures where data points are connected in irregular ways. GSP helps to uncover patterns,…

Imitation Learning Techniques

Imitation learning techniques are methods in artificial intelligence where a computer or robot learns to perform tasks by observing demonstrations, usually from a human expert. Instead of programming every action or rule, the system watches and tries to mimic the behaviour it sees. This approach helps machines learn complex tasks quickly by copying examples, making…

Multi-Objective Reinforcement Learning

Multi-Objective Reinforcement Learning is a type of machine learning where an agent learns to make decisions by balancing several goals at the same time. Instead of optimising a single reward, the agent considers multiple objectives, which can sometimes conflict with each other. This approach helps create solutions that are better suited to real-life situations where…

Reward Sparsity Handling

Reward sparsity handling refers to techniques used in machine learning, especially reinforcement learning, to address situations where positive feedback or rewards are infrequent or delayed. When an agent rarely receives rewards, it can struggle to learn which actions are effective. By using special strategies, such as shaping rewards or providing hints, learning can be made…

Policy Gradient Optimization

Policy Gradient Optimisation is a method used in machine learning, especially in reinforcement learning, to help an agent learn the best actions to take to achieve its goals. Instead of trying out every possible action, the agent improves its decision-making by gradually changing its strategy based on feedback from its environment. This approach directly adjusts…