Neuromorphic AI architectures are computer systems designed to mimic how the human brain works, using networks that resemble biological neurons and synapses. These architectures use specialised hardware and software to process information in a way that is more similar to natural brains than traditional computers. This approach can make AI systems more efficient and better…
Category: Deep Learning
Quantum Machine Learning
Quantum Machine Learning combines quantum computing with machine learning techniques. It uses the special properties of quantum computers, such as superposition and entanglement, to process information in ways that are not possible with traditional computers. This approach aims to solve certain types of learning problems faster or more efficiently than classical methods. Researchers are exploring…
Encrypted Neural Networks
Encrypted neural networks are artificial intelligence models that process data without ever seeing the raw, unprotected information. They use encryption techniques to keep data secure during both training and prediction, so sensitive information like medical records or financial details stays private. This approach allows organisations to use AI on confidential data without risking exposure or…
Neural Network Efficiency
Neural network efficiency refers to how effectively a neural network uses resources such as time, memory, and energy to perform its tasks. Efficient neural networks are designed or optimised to provide accurate results while using as little computation and storage as possible. This is important for running models on devices with limited resources, such as…
Neural Network Generalization
Neural network generalisation is the ability of a trained neural network to perform well on new, unseen data, not just the examples it learned from. It means the network has learned the underlying patterns in the data, instead of simply memorising the training examples. Good generalisation is important for making accurate predictions on real-world data…
Domain-Aware Fine-Tuning
Domain-aware fine-tuning is a process where an existing artificial intelligence model is further trained using data that comes from a specific area or field, such as medicine, law, or finance. This makes the model more accurate and helpful when working on tasks or questions related to that particular domain. By focusing on specialised data, the…
Neural Network Sparsification
Neural network sparsification is the process of reducing the number of connections or weights in a neural network while maintaining its ability to make accurate predictions. This is done by removing unnecessary or less important elements within the model, making it smaller and faster to use. The main goal is to make the neural network…
Contrastive Representation Learning
Contrastive representation learning is a machine learning technique that helps computers learn useful features from data by comparing examples. The main idea is to bring similar items closer together and push dissimilar items further apart in the learned representation space. This approach is especially useful when there are few or no labels for the data,…
Neural Network Compression
Neural network compression is the process of making artificial neural networks smaller and more efficient without losing much accuracy. This is done by reducing the number of parameters, simplifying the structure, or using smart techniques to store and run the model. Compression helps neural networks run faster and use less memory, making them easier to…
Neural Network Calibration
Neural network calibration is the process of adjusting a neural network so that its predicted probabilities accurately reflect the likelihood of an outcome. A well-calibrated model will output a confidence score that matches the true frequency of events. This is important for applications where understanding the certainty of predictions is as valuable as the predictions…