Zobrazeno 1 - 10
of 602
pro vyhledávání: '"Jiao, Yuling"'
Autor:
Wang, Z. T., Chen, Qiuhao, Du, Yuxuan, Yang, Z. H., Cai, Xiaoxia, Huang, Kaixuan, Zhang, Jingning, Xu, Kai, Du, Jun, Li, Yinan, Jiao, Yuling, Wu, Xingyao, Liu, Wu, Lu, Xiliang, Xu, Huikai, Jin, Yirong, Wang, Ruixia, Yu, Haifeng, Zhao, S. P.
To effectively implement quantum algorithms on noisy intermediate-scale quantum (NISQ) processors is a central task in modern quantum technology. NISQ processors feature tens to a few hundreds of noisy qubits with limited coherence times and gate ope
Externí odkaz:
http://arxiv.org/abs/2406.12195
Deep nonparametric regression, characterized by the utilization of deep neural networks to learn target functions, has emerged as a focus of research attention in recent years. Despite considerable progress in understanding convergence rates, the abs
Externí odkaz:
http://arxiv.org/abs/2405.12684
Machine learning is a rapidly advancing field with diverse applications across various domains. One prominent area of research is the utilization of deep learning techniques for solving partial differential equations(PDEs). In this work, we specifica
Externí odkaz:
http://arxiv.org/abs/2405.11451
We propose the characteristic generator, a novel one-step generative model that combines the efficiency of sampling in Generative Adversarial Networks (GANs) with the stable performance of flow-based models. Our model is driven by characteristics, al
Externí odkaz:
http://arxiv.org/abs/2405.05512
This paper aims to conduct a comprehensive theoretical analysis of current diffusion models. We introduce a novel generative learning methodology utilizing the Schr{\"o}dinger bridge diffusion model in latent space as the framework for theoretical ex
Externí odkaz:
http://arxiv.org/abs/2404.13309
We present theoretical convergence guarantees for ODE-based generative models, specifically flow matching. We use a pre-trained autoencoder network to map high-dimensional original inputs to a low-dimensional latent space, where a transformer network
Externí odkaz:
http://arxiv.org/abs/2404.02538
Continuous normalizing flows (CNFs) are a generative method for learning probability distributions, which is based on ordinary differential equations. This method has shown remarkable empirical success across various applications, including large-sca
Externí odkaz:
http://arxiv.org/abs/2404.00551
In this article, we propose a novel Stabilized Physics Informed Neural Networks method (SPINNs) for solving wave equations. In general, this method not only demonstrates theoretical convergence but also exhibits higher efficiency compared to the orig
Externí odkaz:
http://arxiv.org/abs/2403.19090
We introduce an ordinary differential equation (ODE) based deep generative method for learning conditional distributions, named Conditional F\"ollmer Flow. Starting from a standard Gaussian distribution, the proposed flow could approximate the target
Externí odkaz:
http://arxiv.org/abs/2402.01460
We propose SDORE, a semi-supervised deep Sobolev regressor, for the nonparametric estimation of the underlying regression function and its gradient. SDORE employs deep neural networks to minimize empirical risk with gradient norm regularization, allo
Externí odkaz:
http://arxiv.org/abs/2401.04535