Results 291 to 300 of about 24,500,069 (341)

A Combined Approach to Predict Tobacco-Induced Facial Aging Using Dermatologist Knowledge Elicitation and Generative Models. [PDF]

Dermatol Ther (Heidelb)
Raynaud E, Bertrand L, Flament F, Despois J, Ba S, Tancrède-Bohin E, Li T, Jouni H.   +7 more
europepmc   +1 more source

Probabilistic vs Deep Generative Models: A Fairness Centred Evaluation of Synthetic Healthcare Tabular Data

Alattal D, Draghi B, Myles P, Branson R, Tucker A.   +4 more
europepmc   +1 more source

3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models [PDF]

ACM Transactions on Graphics, 2023
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents them as neural fields. The concept
Biao Zhang, Jiapeng Tang, Matthias Niessner   +2 more
exaly   +2 more sources

Generic market models [PDF]

Finance and Stochastics, 2006
The authors introduce generic market models featuring forward rates that span periods other than the classical LIBOR and swap periods. The main outset of the paper is that a model is deemed proper for a certain interest rate derivative if the volatility of a rate that appears in the contract payoff has been calibrated correctly to the market volatility.
Raoul Pietersz, Marcel van Regenmortel
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Generative Model for Node Generation

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence, 2020
We present a generative model applied to graph-structured data for node generation by incorporating the graph convolutional architecture and semi-supervised learning with variational auto-encoder. This idea is motivated by successful applications of deep generative models for images and speeches.
Boyu Zhang, Xin Wang 0086, Kai Liu
openaire   +1 more source

Generative models of the human connectome

NeuroImage, 2016
The human connectome represents a network map of the brain's wiring diagram and the pattern into which its connections are organized is thought to play an important role in cognitive function.
Richard F Betzel, Andrea Avena-Koenigsberger, Joaquin Goni   +2 more
exaly   +2 more sources

A General Model for Adjacency

Fundamenta Informaticae, 1999
The paper considers adjacency relation systems introduced by Wanne [10] (1998). The main problems studied are how to characterize the sets of relations, which uniquely define all other adjacency relations, and how one can determine whether a given set of relations satisfies this property. The paper contains the solutions of both questions.
Merja Wanne, Matti Linna
openaire   +2 more sources

Wan: Open and Advanced Large-Scale Video Generative Models

arXiv.org
This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative ...
Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Xiaofeng Meng, Ningying Zhang, Pandeng Li, Ping Wu, Ruihang Chu, Rui Feng, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, T. Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, Wen-Chao Zhou, Wente Wang, Wen Shen, Wenyuan Yu, Xianzhong Shi, Xiaomin Huang, Xin Xu, Yan Kou, Yan-Mei Lv, Yifei Li, Yijing Liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, Zeyinzi Jiang, Zhen Han, Zhigang Wu, Ziyu Liu   +60 more
semanticscholar   +1 more source

HunyuanVideo: A Systematic Framework For Large Video Generative Models

arXiv.org
Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities ...
Weijie Kong, Qi Tian, Zijian Zhang, Rox Min, Zuozhuo Dai, Jin Zhou, Jia-Liang Xiong, Xin Li, Bo Wu, Jianwei Zhang, Kathrina Wu, Qin Lin, Junkun Yuan, Yanxin Long, Aladdin Wang, Andong Wang, Changlin Li, Duojun Huang, Fan Yang, Hao Tan, Hongmei Wang, Jacob Song, Jiawang Bai, Jianbing Wu, Jinbao Xue, Joey Wang, Kai Wang, Mengyang Liu, Pengyuan Li, Shuai Li, Weiyan Wang, Wenqing Yu, Xi Deng, Yang Li, Yi Chen, Yutao Cui, Yuanbo Peng, Zhen Yu, Zhiyu He, Zhiyong Xu, Zixiang Zhou, Zunnan Xu, Yang-Dan Tao, Qinglin Lu, Songtao Liu, Daquan Zhou, Hongfa Wang, Yong Yang, Di Wang, Yuhong Liu, Jie Jiang, Caesar Zhong   +51 more
semanticscholar   +1 more source

SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers

European Conference on Computer Vision
We present Scalable Interpolant Transformers (SiT), a family of generative models built on the backbone of Diffusion Transformers (DiT). The interpolant framework, which allows for connecting two distributions in a more flexible way than standard ...
Nanye Ma, Mark Goldstein, M. Albergo, N. Boffi, Eric Vanden-Eijnden, Saining Xie   +5 more
semanticscholar   +1 more source