Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...

Not just GPT-3, the previous versions, GPT and GPT-2, too, utilised a decoder only architecture. The original Transformer model is made of both encoder and decoder, where each forms a separate stack.

This comprehensive guide delves into decoder-based Large Language Models (LLMs), exploring their architecture, innovations, and applications in natural language processing. Highlighting the evolution ...

The Encoder-Decoder architecture is a prevalent design in deep learning, especially for tasks like image-to-image translation, which includes use-cases such as image dehazing, segmentation, and ...

Decoder-only transformer models, pre-trained with causal language modeling (LM) objectives, have demonstrated remarkable capabilities. However, their reliance solely on predicting the immediate next ...

In the world of natural language processing, foundation models have typically come in 3 different flavors: Encoder-only (e.g. BERT), Encoder-Decoder (e.g. T5) and Decoder-only (e.g. GPT-*, LLaMA, PaLM ...

In unsupervised medical image registration, encoder-decoder architectures are widely used to predict dense, full-resolution displacement fields from paired images. Despite their popularity, we ...

This paper proposes an Encoder-Decoder neural network architecture with Attention Mechanism for solving the DRC-FJSSP using Deep Q-Learning. In the DRC-FJSSP the number of operations to schedule is ...