Inductive Learning
In Table A, we repeat our experiments on 5000 test examples for each dataset (or the
We thank all reviewers for their valuable comments and suggestions. Table B highlights our differences. Methods on bottom-left corner are better. We will enlarge figures and explain more. In Table 2 and 3, HIGGS contains 10.5 million training examples and the ensemble We additionally added Bosch (1.2 million examples, 968 features) in Table A. Both datasets are from Our method is effective on both datasets.
Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation Bin-Bin Gao
This paper focus on few-shot object detection (FSOD) and instance segmentation (FSIS), which requires a model to quickly adapt to novel classes with a few labeled instances.
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples
Consider binary classification where test examples are not from the training distribution.
Disentangling Human Error from the Ground Truth in Segmentation of Medical Images
Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels.
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks (Author Response)
We thank all the reviewers for their time and constructive comments. In practice, the most cost-effective K might be determined through cross-validation. We will improve the description to make it clear. The existing methods (e.g., Tong & Du [1]) require that the diffusion model is known to us, while our Therefore, the method in Tong & Du [1] is not applicable to our setting, and it is not used as a competitor.