Encoder-Decoder Architecture is a neural network design pattern used to transform one sequence into another, often applied in tasks like machine translation and summarization. It consists of an encoder that processes the input data into a context vector and a decoder that generates the output sequence from this vector, allowing for flexible handling of variable-length sequences.
Residual connections, introduced in ResNet architectures, allow gradients to flow through networks without vanishing by adding the input of a layer to its output. This technique enables the training of much deeper neural networks by effectively addressing the degradation problem associated with increasing depth.
Sequence-to-sequence learning is a neural network framework designed to transform a given sequence into another sequence, which is particularly useful in tasks like machine translation, text summarization, and speech recognition. It typically employs encoder-decoder architectures, often enhanced with attention mechanisms, to handle variable-length input and output sequences effectively.
Query, Key, Value is a fundamental mechanism in the attention mechanism of neural networks, particularly in transformer models, that helps to determine the relevance of input data by calculating a weighted sum of values based on the similarity between queries and keys. This mechanism allows models to focus on specific parts of the input sequence, enhancing the ability to capture dependencies and context over long distances in data sequences.