Category: Deep Learning

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…

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…

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…

Neural Pruning Strategies

Neural pruning strategies refer to methods used to remove unnecessary or less important parts of a neural network, such as certain connections or neurons. The goal is to make the network smaller and faster without significantly reducing its accuracy. This helps in saving computational resources and can make it easier to run models on devices…

Adversarial Example Defense

Adversarial example defence refers to techniques and methods used to protect machine learning models from being tricked by deliberately altered inputs. These altered inputs, called adversarial examples, are designed to look normal to humans but cause the model to make mistakes. Defences help ensure the model remains accurate and reliable even when faced with such…

Neural Network Interpretability

Neural network interpretability is the process of understanding and explaining how a neural network makes its decisions. Since neural networks often function as complex black boxes, interpretability techniques help people see which inputs influence the output and why certain predictions are made. This makes it easier for users to trust and debug artificial intelligence systems,…

Neural Collapse Analysis

Neural Collapse Analysis examines a surprising pattern that arises in the final stages of training deep neural networks for classification tasks. During this phase, the network’s representations for each class become highly organised: the outputs for samples from the same class cluster tightly together, and the clusters for different classes are arranged in a symmetrical,…

Deep Deterministic Policy Gradient

Deep Deterministic Policy Gradient (DDPG) is a machine learning algorithm used for teaching computers how to make decisions in environments where actions are continuous, such as steering a car or controlling a robot arm. It combines two approaches: learning a policy to choose actions and learning a value function to judge how good those actions…

Neural Network Regularization

Neural network regularisation refers to a group of techniques used to prevent a neural network from overfitting to its training data. Overfitting happens when a model learns the training data too well, including its noise and outliers, which can cause it to perform poorly on new, unseen data. Regularisation methods help the model generalise better…