馒头and花卷

摘要： Lahoti P., Beutel A., Chen J., Lee K., Prost F., Thain N., Wang X. and CHi E. H. Fairness without demographics through adversarially reweighted learni 阅读全文

摘要： Dai E. and Wang S. Towards self-explainable graph neural network. In International Conference on Information and Knowledge Management (CIKM), 2021. 概 阅读全文

摘要： Zhu D., Zhang Z., Cui P. and Zhu W. Robust graph convolutional networks against adversarial attacks. In ACM International Conference on Knowledge Disc 阅读全文

摘要： Xu K., Chen H., Liu S., Chen P., Weng T., Hong M. and Lin X. Topology attack and defense for graph neural networks: an optimization perspective. In In 阅读全文

摘要： Olatunji I. E., Funke T. and Khosla M. Releasing graph neural networks with differential privacy guarantees. In ACM Symposium on Neural Gaze Detection 阅读全文

摘要： Papernot N., Abadi M., Erlingsson U., Goodfellow I. and Talwar K. Semi-supervised knowledge transfer for deep learning from private training data. In 阅读全文

摘要： Zhu J., Yan Y., Zhao L., Heimann M., Akoglu L. and Koutra D. Beyond homophily in graph neural networks: current limitations and effective designs. In 阅读全文

摘要： Alon U. and Yahav E. On the bottleneck of graph neural networks and its practical implications. In International Conference on Learning Representation 阅读全文

摘要： Ma Y., Liu X., Zhao T., Liu Y., Tang J. and Shah N. A unified view on graph neural networks as graph signal denoising. In International Conference on 阅读全文

摘要： Gilmer J., Schoenholz S. S., Riley P. F., Vinyals O. and Dahl G. E. Neural message passing for quantum chemistry. In International Conference on Machi 阅读全文

摘要： Zuo Y., Liu G., Lin H., Guo J., Hu X. and Wu J. Embedding temporal network via neighborhood formation. In ACM International Conference on Knowledge Di 阅读全文

摘要： Zhou L., Yang Y., Ren X., Wu F. and Zhuang Y. Dynamic network embedding by modeling triadic closure process. In AAAI Conference on Advancement of Arti 阅读全文

摘要： Grover A. and Leskovec J. node2vec: Scalable Feature Learning for Networks. In ACM International Conference on Knowledge Discovery and Data Mining (KD 阅读全文

摘要： Chen Y. Lecture 4: Importance Sampling and Rejection Sampling. Importance Sampling 设想我们希望估计这样的一个值: $$ I = \mathbb{E}_p(f(X)) = \int f(x)p(x) dx; $$ 但是 阅读全文

摘要： Blanc G. and Rendle S. Adaptive sampled softmax with kernel based sampling. In International Conference on Machine Learning (ICML), 2018. 概 这儿 已经讨论了现在 阅读全文

摘要： Chen J., Lian D., Li Y., Wang B., Zheng K. and Chen E. Cache-augmented inbatch importance resampling for training recommender retriever. In Advances i 阅读全文

摘要： Yi Y., Yang J., Hong L., Cheng D. Z., Heldt L., Kumthekar A., Zhao Z., Wei L. and Chi E. Sampling-bias-corrected neural modeling for large corpus item 阅读全文

摘要： Bengio Y. and Sen\acute{e}cal J. S. Adaptive importance sampling to accelerate training of a neural probabilistic language model. IEEE Transactions on 阅读全文

摘要： Jin W., Liu X., Ma Y., Aggarwal C. and Tang J. Feature overcorrelation in deep graph neural networks: a new perspective. In ACM International Conferen 阅读全文

摘要： 目录概符号说明WL-Test现阶段 GNN 的表达能力GIN其它的注意事项实际的操作代码 Xu K., Hu W., Leskovec J. and Jegelka S. How powerful are graph neural networks? In International Confere 阅读全文