Weight freezing is a technique used in training neural networks where certain layers or parameters are kept unchanged during further training. This means that the values of these weights are not updated by the learning process. It is often used when reusing parts of a pre-trained model, helping to preserve learned features while allowing new…
Fine-tune sets are collections of data specifically chosen to train or adjust an existing artificial intelligence model, making it perform better on a certain task or with a particular type of input. These sets usually contain examples and correct answers, helping the AI learn more relevant patterns and responses. Fine-tuning allows a general model to…
Layer fusion is a technique used in machine learning and computer vision to combine multiple processing steps or layers into a single, more efficient operation. This process helps reduce the amount of computation and can speed up how quickly a model runs. It is especially useful when deploying models on devices with limited resources, such…
Latent injection is a technique used in artificial intelligence and machine learning where information is added or modified within the hidden, or ‘latent’, layers of a model. These layers represent internal features that the model has learned, which are not directly visible to users. By injecting new data or signals at this stage, developers can…
Sequence folding is a process that takes a long list or sequence of items and combines them into a single result by applying a specific operation step by step. The operation is usually something simple, like adding numbers together or joining words, but it can be more complex depending on the task. This method is…
Neural collapse is a phenomenon observed in deep neural networks during the final stages of training, particularly for classification tasks. It describes how the outputs or features for each class become highly clustered and the final layer weights align with these clusters. This leads to a simplified geometric structure where class features and decision boundaries…
Neural activation optimization is a process in artificial intelligence where the activity levels of neurons in a neural network are adjusted for better performance. This involves fine-tuning how much each neuron responds to inputs so that the entire network can learn more effectively and make accurate predictions. The goal is to find the best settings…
Neural Representation Analysis is a method used to understand how information is processed and stored within the brain or artificial neural networks. It examines the patterns of activity across groups of neurons or network units when responding to different stimuli or performing tasks. By analysing these patterns, researchers can learn what kind of information is…
Neural feature analysis is the process of examining and understanding the patterns or characteristics that artificial neural networks use to make decisions. It involves identifying which parts of the input data, such as pixels in an image or words in a sentence, have the most influence on the network’s output. By analysing these features, researchers…
Neural inference analysis refers to the process of examining how neural networks make decisions when given new data. It involves studying the output and internal workings of the model during prediction to understand which features or patterns it uses. This can help improve transparency, accuracy, and trust in AI systems by showing how conclusions are…