Implicit neural representations are a way of storing information like images, 3D shapes or sound using neural networks. Instead of saving data as a grid of numbers or pixels, the neural network learns a mathematical function that can produce any part of the data when asked. This makes it possible to store complex data in…
Category: Deep Learning
Gradient Flow Analysis
Gradient flow analysis is a method used to study how the gradients, or error signals, move through a neural network during training. This analysis helps identify if gradients are becoming too small (vanishing) or too large (exploding), which can make training difficult or unstable. By examining the gradients at different layers, researchers and engineers can…
Multi-Task Learning
Multi-task learning is a machine learning approach where a single model is trained to perform several related tasks at the same time. By learning from multiple tasks, the model can share useful information between them, often leading to better overall performance. This technique can help the model generalise better and make more efficient use of…
Neural Symbolic Integration
Neural Symbolic Integration is an approach in artificial intelligence that combines neural networks, which learn from data, with symbolic reasoning systems, which follow logical rules. This integration aims to create systems that can both recognise patterns and reason about them, making decisions based on both learned experience and clear, structured logic. The goal is to…
Out-of-Distribution Detection
Out-of-Distribution Detection is a technique used to identify when a machine learning model encounters data that is significantly different from the data it was trained on. This helps to prevent the model from making unreliable or incorrect predictions on unfamiliar inputs. Detecting these cases is important for maintaining the safety and reliability of AI systems…
Continual Learning
Continual learning is a method in artificial intelligence where systems are designed to keep learning and updating their knowledge over time, instead of only learning once from a fixed set of data. This approach helps machines adapt to new information or tasks without forgetting what they have already learned. It aims to make AI more…
Memory-Augmented Neural Networks
Memory-Augmented Neural Networks are artificial intelligence systems that combine traditional neural networks with an external memory component. This memory allows the network to store and retrieve information over long periods, making it better at tasks that require remembering past events or facts. By accessing this memory, the network can solve problems that normal neural networks…
Dynamic Neural Networks
Dynamic Neural Networks are artificial intelligence models that can change their structure or operation as they process data. Unlike traditional neural networks, which have a fixed sequence of layers and operations, dynamic neural networks can adapt in real time based on the input or the task at hand. This flexibility allows them to handle a…
Neural Module Networks
Neural Module Networks are a type of artificial intelligence model that break down complex problems into smaller tasks, each handled by a separate neural network module. These modules can be combined in different ways, depending on the question or task, to produce a final answer or result. This approach is especially useful for tasks like…
Pruning-Aware Training
Pruning-aware training is a machine learning technique where a model is trained with the knowledge that parts of it will be removed, or pruned, later. This helps the model maintain good performance even after some connections or neurons are taken out to make it smaller or faster. By planning for pruning during training, the final…