Mar 1, 2025 · Autoencoders consists of two components: Encoder: This compresses the input into a compact representation and capture the most relevant features. Decoder: It reconstructs the input data from this compressed form to make it as similar as possible to the original input.

Aug 16, 2024 · The Encoder-Decoder architecture is a fundamental concept in machine learning, especially in tasks involving sequences such as machine translation, text summarization, and image captioning.

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.

May 30, 2024 · We present new results to model and understand the role of encoder-decoder design in machine learning (ML) from an information-theoretic angle. We use two main information concepts, information sufficiency (IS) and mutual information loss (MIL), to represent predictive structures in machine learning.

Dec 30, 2024 · Autoencoders are types of neural network architecture used for unsupervised learning. The architecture consists of an encoder and a decoder. The encoder encodes the input data into a lower dimensional space while the decoder decodes the …

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...

Jan 12, 2024 · In the field of AI / machine learning, the encoder-decoder architecture is a widely-used framework for developing neural networks that can perform natural language processing (NLP) tasks such as language translation, text summarization, and question-answering systems, etc which require sequence-to-sequence modeling.

Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

Dec 16, 2024 · High level architecture of encoder and decoder. Earlier I have given a very high level overview of each component of the architecture in the blog post Epic History of Large Language Models. Let us read about each component in greater depth now. I will start with the working of encoder first.

Draw the autoencoder architecture and explain when/why it is superior to linear compression. A great many models in machine learning build on the Encoder-Decoder paradigm, but what is the intuition behind said paradigm?