馒头and花卷

摘要： Li B., Wang Y., Singh A. and Vorobeychik Y. Data poisoning attacks on factorization-based collaborative filtering. In Advances in Neural Information P 阅读全文

摘要： [1] Lin X., Zhen H., Li Z., Zhang Q. and Kwong S. Pareto multi-task learning. In Advances in Neural Information Processing Systems (NIPS), 2019. [2] F 阅读全文

摘要： He X., Liao L., Zhang H., Nie L., Hu X. and Chua T. Neural collaborative filtering. In International World Wide Web Conference (WWW), 2017. 概 对用户和物品隐变 阅读全文

摘要： Sener O. and Koltun V. Multi-task learning as multi-objective optimization. In Advances in Neural Information Processing Systems (NIPS), 2018. 概 本文提出的 阅读全文

摘要： D\acute{e}sid\acute{e}ri J.-A. Multiple-gradient descent algorithm (MGDA) for multiobjective optimization. Comptes Rendus Mathematique, vol. 350, pp. 阅读全文

摘要： Sagawa S., Koh P. W., Hashimoto T. B. and Liang P. Distributionally robust neural networks for group shifts: on the importance of regularization for w 阅读全文

摘要： Juan Y., Zhuang Y., Chin W. and Lin C. Field-aware factorization machines for CTR prediction. In ACM Conference on Recommender Systems (RecSys), 2016. 阅读全文

摘要： Rendle S. Factorization machines. In IEEE International Conference on Data Mining (ICDM), 2010 概 SVM在很多领域都有应用, 却在推荐系统中没有什么特别好的效果, 作者认为主要原因是推荐系统的数据过于稀疏 阅读全文

摘要： Lee S., Lee H. and Yoon S. Adversarial vertex mixup: toward better adversarially robust generalization. In IEEE Conference on Computer Vsion and Patte 阅读全文

摘要： Linden G., Smith B. and York J. Amazon.com recommendations item-to-item collaborative filtering. IEEE Internet Computing, 2003. 概 传统的协同过滤绝大部分计算都是onlin 阅读全文

摘要： Wang J., Le Y., Chang B., Wang Y., Chi E. and Chen M. Learning to augment for casual user recommendation. In International World Wide Web Conference ( 阅读全文

摘要： Shan Y., Hoens T. R., Jiao J., Wang H., Yu D. and Mao J. Deep crossing: web-scale modeling without manually crafted combinatorial features. In Interna 阅读全文

摘要： Sedhain S., Menon A. K., Sanner S. and Xie L. AutoRec: autoencoders meet collaborative filtering. In International Conference on World Wide Web (WWW), 阅读全文

摘要： Ishida T., Yamane I., Sakai T., Niu G. and Sugiyama M. Do we need zero training loss after achieving zero training error? In International Conference 阅读全文

摘要： Flooding-X: improving bert’s resistance to adversarial attacks via loss-restricted fine-tuning. 概 作者认为通过 flooding 能够使得 loss landscape 平滑, 这有利于抵抗对抗攻击. 阅读全文

摘要： [1] Monti F., Frasca F., Eynard D., Mannion D. and Bronstein M. M. Fake news detection on social media using geometric deep learning. In International 阅读全文

摘要： [Bruna J., Zaremba W., Szlam A. and LeCun Y. Spectral networks and deep locally connected networks on graphs. In International Conference on Learning 阅读全文

摘要： Horaud R. A Short Tutorial on Graph Laplacians, Laplacian Embedding, and Spectral Clustering. 看GCN的时候对基于谱的来龙去脉不是很理解, 这里先整理下关于 Graph Laplacians 的知识. 符号 阅读全文

摘要： Dong C., Liu L. and Shang J. Double descent in adversarial training: an implicit label noise perspective. In International Conference on Learning Repr 阅读全文

摘要： Dong Y., Xu K., Yang X. Pang T., Deng Z. Su H. and Zhu J. Exploring memorization in adversarial training. In International Conference on Learning Repr 阅读全文