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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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[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
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[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
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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
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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
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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
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Laine S. and Aila T. Temporal ensembling for semi-supervised learning. In International Conference on Learning Representations (ICLR), 2017. 概 本文提出两种半 阅读全文
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Wang Z., Zhang W, Liu N. and Wang J. Transparent classification with multilayer logical perceptrons and random binarization. In AAAI Conference on Art 阅读全文
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Feldman V. and Zhang C. What neural networks memorize and why: discovering the long tail via influence estimation. In Advances in Neural Information P 阅读全文
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Feldman V. Does learning require memorization? a short tale about a long tail. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Com 阅读全文