The conventional focal loss balances the training process with the same modulating factor for all categories, thus failing to handle the long-tailed problem.
This paper identifies and addresses dynamic selection problems that arise in online learning algorithms with endogenous data.
The application of deep learning to medical image segmentation has been hampered due to the lack of abundant pixel-level annotated data.
In the standard data analysis framework, data is first collected (once for all), and then data analysis is carried out.
Then we conducted a motion blur image generation experiment on some general facial data set, and used the pairs of blurred and sharp face image data to perform the training and testing experiments of the processor GAN, and gave some visual displays.
Coarse-to-fine models and cascade segmentation architectures are widely adopted to solve the problem of large scale variations in medical image segmentation.
In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the extracted attention maps of different modalities enable the model to transfer the significant spatial information with more details.
In this paper we develop a data-driven smoothing technique for high-dimensional and non-linear panel data models.
A common problem in econometrics, statistics, and machine learning is to estimate and make inference on functions that satisfy shape restrictions.
In this paper, we provide results for valid inference after post- or orthogonal $L_2$-Boosting is used for variable selection.
In the recent years more and more high-dimensional data sets, where the number of parameters $p$ is high compared to the number of observations $n$ or even larger, are available for applied researchers.
Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting.
They are as convenient and easy to report in practice as the conventional average partial effects.