Neural Networks

An activation function determines the output of a neural network node based on its input.

Attention weight determines the importance of different inputs in neural networks, especially in transformer models.

An autoencoder is a type of neural network used for unsupervised learning, primarily for data compression and feature extraction.

Average pooling reduces the size of feature maps by taking the average value of sub-regions.

Backpropagation is an algorithm used in training neural networks by adjusting weights based on error feedback.

A technique in neural networks that involves propagating errors through complex structures to update weights effectively.

A method for training recurrent neural networks by calculating gradients through time steps.

Bahdanau Attention is a neural network mechanism that enhances focus on relevant parts of input data during processing.

Batch Normalization is a technique to improve training speed and stability in deep neural networks.

A Batch Normalization Layer normalizes inputs to stabilize and accelerate deep learning training.

A bias term is an additional parameter in machine learning models that helps adjust predictions.

A Bidirectional RNN processes data in both forward and backward directions for better context understanding.

A bottleneck block is a component in neural networks that reduces dimensionality and improves efficiency.

A Capsule Network is a type of neural network designed to recognize patterns and preserve spatial relationships in data.

A capsule neural network is an advanced neural network architecture that enhances the ability to recognize patterns and spatial hierarchies.

Capsule Routing is a neural network technique that improves the way data is processed, enhancing accuracy and efficiency.

Catastrophic forgetting refers to the sudden loss of previously learned information when a new task is introduced in AI models.

Channel Attention enhances model focus on important features in AI tasks by weighing channels adaptively.

A committee machine is an ensemble learning model that combines multiple neural networks for improved performance.

A Compressive Transformer is a neural network model that reduces input data size while maintaining essential features for processing.

A Concept Activation Vector (CAV) is a mathematical representation used in AI to identify and quantify concepts in neural networks.

A Conditional Variational Autoencoder (CVAE) is a type of neural network that generates data conditioned on specific input labels.

A framework enabling AI systems to learn continuously from new data without forgetting previous knowledge.

ConvNeXt is a convolutional neural network architecture that enhances performance on vision tasks by combining modern techniques.

A type of deep learning model designed for processing structured grid data, especially images.

A copy mechanism in AI refers to the method of duplicating parts of input data to enhance model performance.

Coverage forgetting refers to the loss of knowledge in AI systems when certain scenarios or data are overlooked during training.

Cyclic Learning Rate is a training technique that varies the learning rate cyclically to improve model performance.