Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Chen, Minshuo"'
Diffusion Transformer, the backbone of Sora for video generation, successfully scales the capacity of diffusion models, pioneering new avenues for high-fidelity sequential data generation. Unlike static data such as images, sequential data consists o
Externí odkaz:
http://arxiv.org/abs/2407.16134
Diffusion models have revolutionized various application domains, including computer vision and audio generation. Despite the state-of-the-art performance, diffusion models are known for their slow sample generation due to the extensive number of ste
Externí odkaz:
http://arxiv.org/abs/2406.16213
Diffusion models have demonstrated empirical successes in various applications and can be adapted to task-specific needs via guidance. This paper studies a form of gradient guidance for adapting a pre-trained diffusion model towards optimizing user-s
Externí odkaz:
http://arxiv.org/abs/2404.14743
An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
Diffusion models, a powerful and universal generative AI technology, have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology. In these applications, diffusion models provide flexible high-dimensio
Externí odkaz:
http://arxiv.org/abs/2404.07771
Generative AI has redefined artificial intelligence, enabling the creation of innovative content and customized solutions that drive business practices into a new era of efficiency and creativity. In this paper, we focus on diffusion models, a powerf
Externí odkaz:
http://arxiv.org/abs/2403.13219
Conditional diffusion models serve as the foundation of modern image synthesis and find extensive application in fields like computational biology and reinforcement learning. In these applications, conditional diffusion models incorporate various con
Externí odkaz:
http://arxiv.org/abs/2403.11968
Diffusion models benefit from instillation of task-specific information into the score function to steer the sample generation towards desired properties. Such information is coined as guidance. For example, in text-to-image synthesis, text input is
Externí odkaz:
http://arxiv.org/abs/2403.01639
A recently popular approach to solving reinforcement learning is with data from human preferences. In fact, human preference data are now used with classic reinforcement learning algorithms such as actor-critic methods, which involve evaluating an in
Externí odkaz:
http://arxiv.org/abs/2310.10556
Policy gradient methods equipped with deep neural networks have achieved great success in solving high-dimensional reinforcement learning (RL) problems. However, current analyses cannot explain why they are resistant to the curse of dimensionality. I
Externí odkaz:
http://arxiv.org/abs/2309.13915
For a real-world decision-making problem, the reward function often needs to be engineered or learned. A popular approach is to utilize human feedback to learn a reward function for training. The most straightforward way to do so is to ask humans to
Externí odkaz:
http://arxiv.org/abs/2307.12975