Neural network activation functions are mathematical formulas used in artificial neural networks to decide whether a neuron should be activated or not. They help the network learn complex patterns by introducing non-linearities, which means the network can handle more complicated tasks. Without activation functions, a neural network would only be able to solve simple problems…
Category: Model Optimisation Techniques
Adaptive Dropout Methods
Adaptive dropout methods are techniques used in training neural networks to prevent overfitting by randomly turning off some neurons during each training cycle. Unlike standard dropout, adaptive dropout adjusts the dropout rate based on the importance or activity of each neuron, allowing the model to learn which parts of the network are most valuable for…
Neural Network Regularisation Techniques
Neural network regularisation techniques are methods used to prevent a model from becoming too closely fitted to its training data. When a neural network learns too many details from the examples it sees, it may not perform well on new, unseen data. Regularisation helps the model generalise better by discouraging it from relying too heavily…
Neural Architecture Transfer
Neural Architecture Transfer is a method where a machine learning model’s structure, or architecture, developed for one task is reused or adapted for a different but related task. Instead of designing a new neural network from scratch, researchers use proven architectures as a starting point and modify them as needed. This approach saves time and…
Neural Network Search Spaces
Neural network search spaces refer to the set of all possible neural network designs that can be considered for a specific task. This includes choices like the number of layers, types of layers, connections between layers, activation functions, and other design options. Exploring this space helps researchers and engineers find the most effective neural network…
Multi-Branch Neural Networks
Multi-branch neural networks are a type of artificial neural network where the architecture splits into two or more separate paths, or branches, at certain points. Each branch can process different pieces of information or apply different transformations before combining their outputs. This structure allows the network to learn multiple types of features or handle different…
Adaptive Residual Networks
Adaptive Residual Networks are a type of artificial neural network that builds on the concept of residual networks, or ResNets, by allowing the network to adjust how much information is passed forward at each layer. In traditional ResNets, shortcut connections let information skip layers, which helps with training deeper networks. Adaptive Residual Networks improve on…
Lightweight Model Architectures for Edge Devices
Lightweight model architectures for edge devices are specially designed artificial intelligence models that use less memory, computing power and energy. These models are made to work efficiently on devices like smartphones, sensors and cameras, which have limited resources compared to powerful computers or servers. The goal is to enable AI functions, such as recognising objects…
Efficient Transformer Variants
Efficient Transformer variants are modified versions of the original Transformer model designed to use less memory and computation. Traditional Transformers can be slow and expensive when working with long texts or large datasets. These variants use clever techniques to make the models faster and less resource-intensive while aiming to keep their accuracy high.
Neural Network Quantisation Techniques
Neural network quantisation techniques are methods used to reduce the size and complexity of neural networks by representing their weights and activations with fewer bits. This makes the models use less memory and run faster on hardware with limited resources. Quantisation is especially valuable for deploying models on mobile devices, embedded systems, or any place…