3 papers
cs.AI2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
Qianli Shen, Wai Hoh Tang, Zhun Deng +2
Standard approaches for uncertainty quantification in deep learning and physics-informed learning have persistent limitations. Indicatively, strong assumptions regarding the data l…
cs.LG2022
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Yingtian Zou, Vikas Verma, Sarthak Mittal +6
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.…
stat.ME2018
Model identification for ARMA time series through convolutional neural networks
Wai Hoh Tang, Adrian Röllin
In this paper, we use convolutional neural networks to address the problem of model identification for autoregressive moving average time series models. We compare the performance…