Results 1 to 10 of about 572,440 (170)
Q-Diffusion: Quantizing Diffusion Models
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 The code is available at https://github.com/Xiuyu-Li/q ...
Xiuyu Li +7 more
openaire +2 more sources
Advances in Neural Information Processing Systems 35, 2022 Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation.
Chin-Wei Huang +4 more
openaire +3 more sources
Advances in Neural Information Processing Systems 35, 2022 Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architecture, and it enables jointly ...
Jonathan Ho +5 more
openaire +3 more sources
CoRR, 2022 Recently, Rissanen et al., (2022) have presented a new type of diffusion process for generative modeling based on heat dissipation, or blurring, as an alternative to isotropic Gaussian diffusion. Here, we show that blurring can equivalently be defined through a Gaussian diffusion process with non-isotropic noise.
Emiel Hoogeboom, Tim Salimans
openaire +3 more sources
On the Generalization of Diffusion Model
CoRR, 2023 The diffusion probabilistic generative models are widely used to generate high-quality data. Though they can synthetic data that does not exist in the training set, the rationale behind such generalization is still unexplored. In this paper, we formally define the generalization of the generative model, which is measured by the mutual information ...
Mingyang Yi, Jiacheng Sun, Zhenguo Li
openaire +2 more sources
Renormalizing Diffusion Models
CoRR, 2023 69+15 pages, 8 figures; v2: figure and references added, typos ...
Jordan Cotler, Semon Rezchikov
openaire +2 more sources
Optimizing CELF Algorithm for Influence Maximization Problem in Social Networks [PDF]
Journal of Artificial Intelligence and Data Mining, 2022 The Influence Maximization Problem in social networks aims to find a minimal set of individuals to produce the highest influence on other individuals in the network.
M. Taherinia +2 more
doaj +1 more source
CoRR, 2023 Diffusion models (DMs) have been adopted across diverse fields with its remarkable abilities in capturing intricate data distributions. In this paper, we propose a Fast Diffusion Model (FDM) to significantly speed up DMs from a stochastic optimization perspective for both faster training and sampling.
Zike Wu +3 more
openaire +2 more sources
CoRR, 2023 The availability and accessibility of diffusion models (DMs) have significantly increased in recent years, making them a popular tool for analyzing and predicting the spread of information, behaviors, or phenomena through a population. Particularly, text-to-image diffusion models (e.g., DALLE 2 and Latent Diffusion Models (LDMs) have gained significant
Yugeng Liu +4 more
openaire +2 more sources
Applied Sciences, 2023 Inlight of the extensive utilization of automated machining centers, the operation and maintenance level and efficiency of machining centers require further enhancement.
Jiewen Huang, Ying Yang
doaj +1 more source