Performance Analysis
Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training
Most existing point cloud detection models require large-scale, densely annotated datasets.
A Comparison with Other General MLCO Frameworks
We would also like to discuss the limitations of the approaches including ours. As shown in Tab. 4, the PPO-Single that serves as a baseline in our paper is designed following As shown in Tab. 4, NerRewritter is most general because it can be viewed as a learning-based local It is also worth noting that there are some problems that are beyond our knowledge to tackle, e.g. the expression simplify problem, and it may requires experts with specific domain We have discussed the model details of PPO-BiHyb in Sec. 4, and in this section, we discuss the DAG. Considering the structure of DAG, we design two GCNs: the first GCN processes the original DAG, and the second GCN processes the DAG with all edges reversed. The predicted doubly-stochastic matrix by SK is processed by considering the partial matching matrix. Graph-level features are obtained via attention pooling, which are fed to the critic net.
about real-world experiments and deep density models and then answer detailed comments and questions
We thank the reviewers for their very helpful comments and suggestions. The 100% recall but very poor precision (i.e., it always predicts a shift) is expected Marginal-KS is very bad because the attack model is very strong, i.e., it mimics the marginal distribution of Thus, marginal KS will naturally fail--highlighting the limitation of prior work for this adversarial attack. F or bootstrapping, does the model need to be fit multiple times? For Gaussian, this is fairly simple. For the detection stage, the FDR was controlled below 0.05 in all See also Table 6 and 7 in appendix.
Table 1 Additional experiments in terms of classification Accuracy
We thank the reviewers for their valuable comments. We will add suggested experiments, references, and fix typos in the updated version. Take the large-scale ImageNet as an example. A3: Please refer to Table 1a and A2@R#1 for additional results on ImageNet. We see that the weights on the diagonal are higher.
No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems
We then use these approximate subclass labels as a form of noisy supervision in a distributionally robust optimization objective.
Target alignment in truncated kernel ridge regression
We show that TKRR can achieve a fast rate of convergence in the over-aligned regime.