Category: Model Training & Tuning

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…

Robustness-Aware Training

Robustness-aware training is a method in machine learning that focuses on making models less sensitive to small changes or errors in input data. By deliberately exposing models to slightly altered or adversarial examples during training, the models learn to make correct predictions even when faced with unexpected or noisy data. This approach helps ensure that…

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…

Multi-Task Learning Frameworks

Multi-Task Learning Frameworks are systems or methods that train a single machine learning model to perform several related tasks at once. By learning from multiple tasks together, the model can share useful information between them, which often leads to better results than learning each task separately. These frameworks are especially helpful when tasks are similar…

Neural Architecture Pruning

Neural architecture pruning is a technique used to make artificial neural networks smaller and faster by removing unnecessary or less important parts. This process helps reduce the size and complexity of a neural network without losing much accuracy. By carefully selecting which neurons or connections to remove, the pruned network can still perform its task…

Cross-Domain Transferability

Cross-domain transferability refers to the ability of a model, skill, or system to apply knowledge or solutions learned in one area to a different, often unrelated, area. This concept is important in artificial intelligence and machine learning, where a model trained on one type of data or task is expected to perform well on another…

Model-Agnostic Meta-Learning

Model-Agnostic Meta-Learning, or MAML, is a machine learning technique designed to help models learn new tasks quickly with minimal data. Unlike traditional training, which focuses on one task, MAML prepares a model to adapt fast to many different tasks by optimising it for rapid learning. The approach works with various model types and does not…

Continual Learning Benchmarks

Continual learning benchmarks are standard tests used to measure how well artificial intelligence systems can learn new tasks over time without forgetting previously learned skills. These benchmarks provide structured datasets and evaluation protocols that help researchers compare different continual learning methods. They are important for developing AI that can adapt to new information and tasks…

Self-Adaptive Neural Networks

Self-adaptive neural networks are artificial intelligence systems that can automatically adjust their own structure or learning parameters as they process data. Unlike traditional neural networks that require manual tuning of architecture or settings, self-adaptive networks use algorithms to modify layers, nodes, or connections in response to the task or changing data. This adaptability helps them…