Kaleido Diffusion: Improving Conditional Diffusion Models with Autoregressive Latent Modeling
Diffusion models have become a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often ...
Tag: autoregressive
Multimodal Autoregressive Pretraining of Wide View Encoders
*Equal taxpayers A dominant paradigm in large multimodal models is to pair a large language decoder with a vision encoder. ...
Generalizable autoregressive modeling of time series through functional narratives
Time series data are inherently functions of time, however current transformers often learn time series by modeling them as mere ...
LOONG – A new LLM-based autoregressive video generator that can generate one-minute long videos
LLM video generation is an emerging field with a promising growth trajectory. While large language autoregressive models (LLMs) have excelled ...
AiM: An Autoregressive (AR) Generative Image Model Based on the Mamba Architecture
Large language models (LLMs) based on autoregressive transformer decoder architectures have advanced natural language processing with exceptional performance and scalability. ...
Eliminating Vector Quantization: Diffusion-Based Autoregressive AI Models for Image Generation
Autoregressive imaging models have traditionally relied on vector-quantized representations, which introduces several important challenges. The vector quantization process requires a ...
From masked image modeling to autoregressive image modeling | by Mengliu Zhao | June 2024
Previous training in image domainMoving to the image domain, the immediate question is how we form the “token sequence” of ...
ALPINE: Autoregressive learning for network planning
Large language models (LLMs), such as ChatGPT, have attracted a lot of attention because they can perform a wide range ...
Princeton and Meta AI researchers present 'Lory': a fully differentiable MoE model designed for autoregressive language model pre-training
Merge-of-experts (MoE) architectures use sparse activation to initialize scaling of model sizes while preserving high inference and training efficiency. However, ...
CMU researchers present 'Echo Embeddings': an embedding strategy designed to address an architectural limitation of autoregressive models
Neural text embeddings play a critical role in many modern natural language processing (NLP) applications. These embeddings are like fingerprints ...
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