Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Liao Wenjing"'
Autor:
Havrilla, Alex, Liao, Wenjing
When training deep neural networks, a model's generalization error is often observed to follow a power scaling law dependent both on the model size and the data size. Perhaps the best known example of such scaling laws are for transformer-based large
Externí odkaz:
http://arxiv.org/abs/2411.06646
Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a complete theoretical framework for understanding these scaling laws remains underdeveloped. In this paper,
Externí odkaz:
http://arxiv.org/abs/2410.00357
Deep learning has exhibited remarkable results across diverse areas. To understand its success, substantial research has been directed towards its theoretical foundations. Nevertheless, the majority of these studies examine how well deep neural netwo
Externí odkaz:
http://arxiv.org/abs/2406.05320
Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data,
Externí odkaz:
http://arxiv.org/abs/2401.10490
Diffusion generative models have achieved remarkable success in generating images with a fixed resolution. However, existing models have limited ability to generalize to different resolutions when training data at those resolutions are not available.
Externí odkaz:
http://arxiv.org/abs/2401.06144
Existing theories on deep nonparametric regression have shown that when the input data lie on a low-dimensional manifold, deep neural networks can adapt to the intrinsic data structures. In real world applications, such an assumption of data lying ex
Externí odkaz:
http://arxiv.org/abs/2306.14859
We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from a single trajectory of noisy observations. Identifying unknown differential equations from noisy observations
Externí odkaz:
http://arxiv.org/abs/2304.05543
Autoencoders have demonstrated remarkable success in learning low-dimensional latent features of high-dimensional data across various applications. Assuming that data are sampled near a low-dimensional manifold, we employ chart autoencoders, which en
Externí odkaz:
http://arxiv.org/abs/2303.09863
Generative networks have experienced great empirical successes in distribution learning. Many existing experiments have demonstrated that generative networks can generate high-dimensional complex data from a low-dimensional easy-to-sample distributio
Externí odkaz:
http://arxiv.org/abs/2302.13183
Autor:
Liu, Qinwei1,2 (AUTHOR) 3200103801@zju.edu.cn, Liao, Wenjing3 (AUTHOR), Yang, Li1 (AUTHOR), Cao, Longfei4 (AUTHOR), Liu, Ningning5,6 (AUTHOR), Gu, Yongxue7 (AUTHOR), Wang, Shaohua8 (AUTHOR), Xu, Xiaobin1,9 (AUTHOR) xuxiaobin@zju.edu.cn, Wang, Huafen1 (AUTHOR) 2185015@zju.edu.cn
Publikováno v:
BMC Psychiatry. 12/18/2024, Vol. 24 Issue 1, p1-11. 11p.
