1 code implementation • 20 Feb 2024 • Hongxin Wei, Jianguo Huang
TorchCP is a Python toolbox for conformal prediction research on deep learning models.
no code implementations • 6 Feb 2024 • Huajun Xi, Jianguo Huang, Lei Feng, Hongxin Wei
Conformal prediction, as an emerging uncertainty qualification technique, constructs prediction sets that are guaranteed to contain the true label with high probability.
no code implementations • 1 Feb 2024 • Jianguo Huang, Yue Qiu
Neural operators (NO) are discretization invariant deep learning methods with functional output and can approximate any continuous operator.
2 code implementations • 10 Oct 2023 • Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao, Yue Qiu, Hongxin Wei
In this paper, we empirically and theoretically show that disregarding the probabilities' value will mitigate the undesirable effect of miscalibrated probability values.
no code implementations • 30 Jun 2023 • Yuhuang Meng, Jianguo Huang, Yue Qiu
In Koopman operator theory, a finite-dimensional nonlinear system is transformed into an infinite but linear system using a set of observable functions.
no code implementations • 15 Dec 2020 • Fan Chen, Jianguo Huang, Chunmei Wang, Haizhao Yang
This paper proposes Friedrichs learning as a novel deep learning methodology that can learn the weak solutions of PDEs via a minmax formulation, which transforms the PDE problem into a minimax optimization problem to identify weak solutions.
no code implementations • 22 Jun 2013 • Wei Gao, Jie Chen, Cédric Richard, Jianguo Huang
Unfortunately, an undesirable characteristic of these methods is that the order of the filters grows linearly with the number of input data.