架构

Encoder-Decoder

定义

A two-part neural network architecture where the encoder compresses an input into a latent representation and the decoder generates an output from it. Used in translation, summarisation, and speech recognition systems.

相关术语

Transformer

The dominant neural network architecture for language, vision, and multimodal AI, introduced in the 2017 "Attention Is All You Need" paper. Transformers use self-attention to process all tokens in parallel, enabling training on internet-scale data and powering every major LLM in use today.

Autoencoder

A neural network trained to compress data into a lower-dimensional latent space and reconstruct it. Autoencoders are used for anomaly detection, dimensionality reduction, and as components in generative models like VAEs.

BERT

Bidirectional Encoder Representations from Transformers—Google's 2018 encoder-only transformer model that set new benchmarks across NLP tasks. BERT-family models remain widely used for classification, named-entity recognition, and information retrieval in enterprise settings.

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