Oceania
Appendix for " Topic Modeling Revisited: A Document Graph-based Neural Network Perspective " A Appendix
Here, we display additional material to support our content in the main manuscript, including: - The mathematical notations in Table S1. The word set for the document d, i.e., V The word edge set for the document d . The placeholder set for the document d, i.e., V The placeholder edge set for the document d. The parameter to describe the distribution over word edges for k -th topic. The word edge set among the nodes with the topic assignment k in the document d.
Supplementary Material for "K-L ITE: Learning Transferable Visual Models with External Knowledge "
This appendix is organized as follows. In Section A (referred by CheckList), we discuss the societal impact. In Section B.1 (referred by Section 4.1), we summarize the statistics of the datasets used in In Section B.2 (referred by Section 4), we introduce the pre-training and model adaptation In Section B.3, we provide zero-shot retrieval comparison by introducing knowledge. In Section B.4, we provide quantitative analysis on how external knowledge benefit transfer. In Section B.5 (referred by Section 4.2 and 4.3), we provide more visualizations of success In Section B.6, we provide more object detection results by training on larger dataset for We do not anticipate a specific negative impact, but, as with any Machine Learning method, we recommend to exercise caution.
Appendix A CommonsenseQA Error Patterns Throughout our experiments, we came across a variety of interesting failure cases for commonse
One key failure case was answers in the form of "the answer must be something that is