Sparse Gaussian Processes are a way to make a type of machine learning model called a Gaussian Process faster and more efficient, especially when dealing with large data sets. Normally, Gaussian Processes can be slow and require a lot of memory because they try to use all available data to make predictions. Sparse Gaussian Processes…
Variational inference is a method used in statistics and machine learning to estimate complex probability distributions. Instead of calculating exact values, which can be too difficult or slow, it uses optimisation techniques to find an easier distribution that is close enough to the original. This helps to make predictions or understand data patterns when working…
Neural network pruning is a technique used to reduce the size and complexity of artificial neural networks by removing unnecessary or less important connections, neurons, or layers. This process helps make models smaller and faster without significantly affecting their accuracy. Pruning often follows the training of a large model, where the least useful parts are…
Weight-Agnostic Neural Networks are a type of artificial neural network designed so that their structure can perform meaningful tasks before the weights are even trained. Instead of focusing on finding the best set of weights, these networks are built to work well with a wide range of fixed weights, often using the same value for…
Hypernetwork architectures are neural networks designed to generate the weights or parameters for another neural network. Instead of directly learning the parameters of a model, a hypernetwork learns how to produce those parameters based on certain inputs or contexts. This approach can make models more flexible and adaptable to new tasks or data without requiring…
Bayesian Optimisation is a method for finding the best solution to a problem when evaluating each possible option is expensive or time-consuming. It works by building a model of the problem and using it to predict which options are most promising to try next. This approach is especially useful when you have limited resources or…
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,…
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 combinatorial optimisation is a method that uses neural networks to solve complex problems where the goal is to find the best combination or arrangement from many possibilities. These problems are often difficult for traditional computers because there are too many options to check one by one. By learning from examples, neural networks can quickly…
Trust Region Policy Optimisation, or TRPO, is a method used in reinforcement learning to help computers learn how to make decisions. It works by ensuring that each learning step does not move too far from the previous strategy, which keeps learning stable and prevents sudden mistakes. By carefully controlling how much the computer’s decision-making policy…