Jan 12, 2024 · An autoencoder is a type of neural network architecture that uses an encoder to compress an input into a lower-dimensional representation, and a decoder to reconstruct the original input from the compressed representation.

Oct 17, 2021 · How does an Encoder-Decoder work and why use it in Deep Learning? The Encoder-Decoder is a neural network discovered in 2014 and it is still used today in many projects. It is a fundamental pillar of Deep Learning. It is found in particular in translation software.

Aug 16, 2024 · In this tutorial, we’ll dive deep into what this architecture is, how it works, and why it’s so powerful. 1. Introduction to Encoder-Decoder Architecture. At its core, the Encoder-Decoder...

Encoder-decoder is a type of neural network architecture used for sequential data processing and generation. In deep learning, the encoder-decoder architecture is a type of neural network most widely associated with the transformer architecture and used in sequence-to-sequence learning.

Jun 24, 2023 · An encoder-decoder is a type of neural network architecture that is used for sequence-to-sequence learning. It consists of two parts, the encoder and the decoder. The encoder processes an...

Feb 13, 2025 · Encoder-Decoder models and Recurrent Neural Networks are probably the most natural way to represent text sequences. In this tutorial, we’ll learn what they are, different architectures, applications, issues we could face using them, and what are the most effective techniques to overcome those issues.

Dec 18, 2023 · Function: Decoders are designed to reconstruct or generate output data from an internal representation, effectively translating complex data structures into a more interpretable form. Usage: They...

Aug 13, 2021 · In this chapter, we present formal definitions and mathematical formulations of neural encoding and decoding problems from a Bayesian perspective and provide two case studies involving electrophysiology and magnetoencephalography (MEG) data.

Encoders and decoders are complementary components in neural networks that handle input processing and output generation, respectively. An encoder converts input data into a compact, structured representation (often called a latent vector or context), while a decoder uses this representation to reconstruct data or generate new outputs.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.