Mamba, a recent selective structured state space model, excels in long sequence modeling,
which is vital in the large model era. Long sequence modeling poses significant challenges …

As one of the most popular and sought-after generative models in the recent years, diffusion
models have sparked the interests of many researchers and steadily shown excellent …

Diffusion Transformer (DiT), an emerging diffusion model for image generation, has
demonstrated superior performance but suffers from substantial computational costs. Our …

Modern diffusion models, particularly those utilizing a Transformer-based UNet for
denoising, rely heavily on self-attention operations to manage complex spatial relationships …

\textit {Nature is infinitely resolution-free}. In the context of this reality, existing diffusion
models, such as Diffusion Transformers, often face challenges when processing image …

Sequential modeling has demonstrated remarkable capabilities in offline reinforcement
learning (RL), with Decision Transformer (DT) being one of the most notable …

Mamba is emerging as a novel approach to overcome the challenges faced by
Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) in computer vision …

Transformer, a deep neural network architecture, has long dominated the field of natural
language processing and beyond. Nevertheless, the recent introduction of Mamba …

Image generation models have encountered challenges related to scalability and quadratic
complexity, primarily due to the reliance on Transformer-based backbones. In this study, we …

Autoregressive modeling has been a huge success in the field of natural language
processing (NLP). Recently, autoregressive models have emerged as a significant area of …