Abstract: The foundation of current large language model applications lies in the generative language model, which typically employs an autoregressive token generation approach. However, this model ...

1 School of Environment, Education and Development, University of Manchester, Manchester, United Kingdom 2 Duke University, Durham, NC, United States In financial production systems, accurate risk ...

Abstract: The pre-training architectures of large language models encompass various types, including autoencoding models, autoregressive models, and encoder-decoder models. We posit that any modality ...

This repository implements PixelCNN, an autoregressive model for image generation. It generates images pixel by pixel using masked convolutions to maintain directional dependencies. During inference, ...

Autoregressive Transformer models have demonstrated impressive performance in video generation, but their sequential token-by-token decoding process poses a major bottleneck, particularly for long ...

Researchers developed a hybrid AI approach that can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine times faster and uses ...

Large language models (LLMs) based on autoregressive Transformer Decoder architectures have advanced natural language processing with outstanding performance and scalability. Recently, diffusion ...

We introduce LlamaGen, a new family of image generation models that apply original next-token prediction paradigm of large language models to visual generation domain. It is an affirmative answer to ...

The advent of GPT models, along with other autoregressive or AR large language models har unfurled a new epoch in the field of machine learning, and artificial intelligence. GPT and autoregressive ...