For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We ...

Every time a language model like GPT-4, Claude or Mistral generates a sentence, it does something deceptively simple: It picks one word at a time. This word-by-word approach is what gives ...

One of the core problems with AI is the notoriously high power and computing demand, especially for tasks such as media generation. On mobile phones, when it comes to running natively, only a handful ...

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...

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

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 ...

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 ...

Climate change is a pressing global issue. Mathematical models and global climate models have traditionally been invaluable tools in understanding the Earth’s climate system, however there are several ...

Forecasting solar irradiance is a critical task in the renewable energy sector, as it provides essential information regarding the potential energy production from solar panels. This study aims to ...