Category: Artificial Intelligence

Neural ODE Solvers

Neural ODE solvers are machine learning models that use the mathematics of differential equations to predict how things change over time. Instead of using traditional layers like in standard neural networks, they treat the system as a continuous process and learn how it evolves. This approach allows for flexible and efficient modelling of time-dependent data,…

Sim-to-Real Transfer

Sim-to-Real Transfer is a technique in robotics and artificial intelligence where systems are trained in computer simulations and then adapted for use in the real world. The goal is to use the speed, safety, and cost-effectiveness of simulations to develop skills or strategies that can work outside the virtual environment. This process requires addressing differences…

Domain Randomisation

Domain randomisation is a technique used in artificial intelligence, especially in robotics and computer vision, to make models more robust. It involves exposing a model to many different simulated environments where aspects like lighting, textures, and object positions are changed randomly. By training on these varied scenarios, the model learns to perform well even when…

Neural-Symbolic Reasoning

Neural-symbolic reasoning is a method that combines neural networks, which are good at learning patterns from data, with symbolic reasoning systems, which use rules and logic to draw conclusions. This approach aims to create intelligent systems that can both learn from experience and apply logical reasoning to solve problems. By blending these two methods, neural-symbolic…

Differentiable Programming

Differentiable programming is a method of writing computer programs so that their behaviour can be automatically adjusted using mathematical techniques. This is done by making the entire program differentiable, meaning its outputs can be smoothly changed in response to small changes in its inputs or parameters. This approach allows computers to learn or optimise tasks…

Neural Program Synthesis

Neural program synthesis is a field within artificial intelligence where neural networks are trained to automatically generate computer programmes from examples or descriptions. This approach uses large datasets and deep learning models to learn how to translate tasks or specifications into executable code. The goal is to help automate or assist the process of writing…

End-to-End Memory Networks

End-to-End Memory Networks are a type of artificial intelligence model designed to help computers remember and use information over several steps. They combine a memory component with neural networks, allowing the model to store facts and retrieve them as needed to answer questions or solve problems. This approach is especially useful for tasks where the…

Memory Networks

Memory networks are a type of artificial intelligence model designed to help machines remember and use information over time. They combine traditional neural networks with a memory component, allowing the system to store important facts and retrieve them when needed. This helps the AI perform tasks that require recalling previous details or context, such as…

Differentiable Neural Computers

Differentiable Neural Computers (DNCs) are a type of artificial intelligence model that combines neural networks with an external memory system, allowing them to store and retrieve complex information more effectively. Unlike standard neural networks, which process information in a fixed way, DNCs can learn how to read from and write to memory, making them better…