随笔分类 - Representation Learning
摘要：Rombach R., Blattmann A., Lorenz D., Esser P. and Ommer B. High-resolution image synthesis with latent diffusion models. In IEEE Computer Vision and P
阅读全文
摘要：Zhang M., Cui Z., Jiang S. and Chen Y. Beyond link prediction: Predicting hyperlinks in adjacency space. In AAAI Conference on Advancement of Artifici
阅读全文
摘要：Zhang M. and Chen Y. Link prediction based on graph neural networks. In Advances in Neural Information Processing Systems (NIPS), 2018. 概 这篇工作 的延续的工作.
阅读全文
摘要：Zhang M. and Chen Y. Weisfeiler-Lehman neural machine for link prediction. In ACM International Conference on Knowledge Discovery and Data Mining (KDD
阅读全文
摘要：Pan L., Shi C., Dokmani{'c} I. Neural link prediction with walk pooling. In International Conference on Learning Representations (ICLR), 2022. 概 作者认为
阅读全文
摘要：Cai X., Huang C., Xia L. and Ren X. LightGCL: Simple yet effective graph contrastive learning for recommendation. In International Conference on Learn
阅读全文
摘要：Song J., Meng C. and Ermon S. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021. 概 DDIM 从另一种观点
阅读全文
摘要：Choi J., Lee J., Shin C., Kim S., Kim H. and Yoon S. Perception prioritized training of diffusion models. In IEEE Computer Vision and Pattern Recognit
阅读全文
摘要：Gong S., Li M., Feng J., Wu Z. and Kong L. DiffuSeq: Sequence to sequence text generation with diffusion models. In International Conference on Learni
阅读全文
摘要：Li X. L., Thickstun J., Gulrajani I., Liang P. and Hashimoto T. B. Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217
阅读全文
摘要：Choi J., Hong S., Park N. and Cho S. GREAD: graph neural reaction-diffusion equations. arXiv preprint arXiv: arXiv:2211.14208 概 作者提出了一种基于 reaction-dif
阅读全文
摘要：Chen R. T. Q., Rubanova Y., Bettencourt J. and Duvenaud D. Neural ordinary differential equations. In Advances in Neural Information Processing System
阅读全文
摘要：Alemi A. A., Fischer I., Dillon J. V. and Murphy K. Deep variational information bottleneck. In International Conference on Learning Representations (
阅读全文
摘要：Gasteiger J., Bojchevski A. and G\ddot{u}nnemann S. Predict then propagate: graph neural networks meet personalized pagerank. In International Confere
阅读全文
摘要：Gasteiger J., Weißenberger S., Günnemann S. Diffusion improves graph learning. In Advances in Neural Information Processing Systems (NIPS), 2019. 概 传统
阅读全文
摘要：Ying R., He R., Chen K., Eksombatchai P., Hamilton W. L. and Leskovec J. Graph convolutional neural networks for web-scale recommender systems. In ACM
阅读全文
摘要：Liu S., Ying R., Dong H., Lin L., Chen J., Wu D. How powerful is implicit denoising in graph neural networks? arXiv preprint arXiv: 2209.14514, 2022.
阅读全文
摘要：Zhu M., Wang X., Shi C., Ji H. and Cui P. Interpreting and unifying graph neural networks with an optimization framework. In International World Wide
阅读全文
摘要：Zhao L. and Akoglu L. Connecting graph convolution and graph pca. 2022. 概 从 graph-regularized PCA 角度提出一种 GCN 的 message passing layer. 符号说明
阅读全文
摘要：Liu X., Jin W., Ma Y., Li Y., Li Y., Liu H., Wang Y., Yan M. and Tang J. Elastic graph neural networks. In International Conference on Machine Learnin
阅读全文
