Knowledge-augmented inference is a method where artificial intelligence systems use extra information from external sources to improve their understanding and decision-making. Instead of relying only on what is directly given, the system looks up facts, rules, or context from databases, documents, or knowledge graphs. This approach helps the AI make more accurate and informed conclusions,…
Category: Artificial Intelligence
Neural Attention Scaling
Neural attention scaling refers to the methods and techniques used to make attention mechanisms in neural networks work efficiently with very large datasets or models. As models grow in size and complexity, calculating attention for every part of the data can become extremely demanding. Scaling solutions aim to reduce the computational resources needed, either by…
Causal Representation Learning
Causal representation learning is a method in machine learning that focuses on finding the underlying cause-and-effect relationships in data. It aims to learn not just patterns or associations, but also the factors that directly influence outcomes. This helps models make better predictions and decisions by understanding what actually causes changes in the data.
Uncertainty-Aware Models
Uncertainty-aware models are computer models designed to estimate not only their predictions but also how confident they are in those predictions. This means the model can communicate when it is unsure about its results. Such models are useful in situations where making a wrong decision could be costly or risky, as they help users understand…
Neural Network Disentanglement
Neural network disentanglement is the process of making sure that different parts of a neural network learn to represent different features of the data, so each part is responsible for capturing a specific aspect. This helps the network learn more meaningful, separate concepts rather than mixing everything together. With disentangled representations, it becomes easier to…
Temporal Knowledge Graphs
Temporal Knowledge Graphs are data structures that store information about entities, their relationships, and how these relationships change over time. Unlike standard knowledge graphs, which show static connections, temporal knowledge graphs add a time element to each relationship, helping track when things happen or change. This allows for more accurate analysis of events, trends, and…
Graph-Based Knowledge Fusion
Graph-based knowledge fusion is a technique for combining information from different sources by representing data as nodes and relationships in a graph structure. This method helps identify overlaps, resolve conflicts, and create a unified view of knowledge from multiple datasets. By using graphs, it becomes easier to visualise and manage complex connections between pieces of…
Zero-Knowledge Machine Learning
Zero-Knowledge Machine Learning is a method that allows someone to prove they have trained a machine learning model or achieved a particular result without revealing the underlying data or the model itself. This approach uses cryptographic techniques called zero-knowledge proofs, which let one party convince another that a statement is true without sharing any of…
Neural Compression Algorithms
Neural compression algorithms use artificial neural networks to reduce the size of digital data such as images, audio, or video. They learn to find patterns and redundancies in the data, allowing them to represent the original content with fewer bits while keeping quality as high as possible. These algorithms are often more efficient than traditional…
Semantic Knowledge Injection
Semantic knowledge injection is the process of adding meaningful information or context to a computer system, such as a machine learning model or database, so it can understand and use that knowledge more effectively. This often involves including facts, relationships, or rules about a subject, rather than just raw data. By doing this, the system…