Category: Deep Learning

Optical Neural Networks

Optical neural networks are artificial intelligence systems that use light instead of electricity to perform calculations and process information. They rely on optical components like lasers, lenses, and light modulators to mimic the way traditional neural networks operate, but at much faster speeds and with lower energy consumption. By processing data with photons rather than…

Spiking Neural Networks

Spiking Neural Networks, or SNNs, are a type of artificial neural network designed to work more like the human brain. They process information using spikes, which are brief electrical pulses, rather than continuous signals. This makes them more energy efficient and suitable for certain tasks. SNNs are particularly good at handling data that changes over…

Causal Effect Variational Autoencoders

Causal Effect Variational Autoencoders are a type of machine learning model designed to learn not just patterns in data, but also the underlying causes and effects. By combining ideas from causal inference and variational autoencoders, these models aim to separate factors that truly cause changes in outcomes from those that are just correlated. This helps…

Neural Network Pruning

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

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

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…

Physics-Informed Neural Networks

Physics-Informed Neural Networks, or PINNs, are a type of artificial intelligence model that learns to solve problems by combining data with the underlying physical laws, such as equations from physics. Unlike traditional neural networks that rely only on data, PINNs also use mathematical rules that describe how things work in nature. This approach helps the…

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,…

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…