The Encoder-Decoder architecture is widely used in sequence-to-sequence tasks such as machine translation, text summarization, and speech recognition. However, traditional Encoder-Decoder models face ...

Speech enhancement (SE) models based on deep neural networks (DNNs) have shown excellent denoising performance. However, mainstream SE models often have high structural complexity and large parameter ...

Collection of Natural Language Processing and Machine Learning implementations focusing on: Information Retrieval & RAG Systems LLM Fine-tuning Feature Engineering Neural Architecture Components ...

Encoder-Decoder Architecture. Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks: an encoder and a decoder. The encoder uses stacked multi-head ...

Citation: Aboutalebi H, Pavlova M, Gunraj H, Shafiee MJ, Sabri A, Alaref A and Wong A (2022) MEDUSA: Multi-Scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image ...

An encoder-decoder architecture is a powerful tool used in machine learning, specifically for tasks involving sequences like text or speech. It’s like a two-part machine that translates one form ...