馒头and花卷

摘要： 目录概SEAL代码 Bevilacqua M., Ottaviano G., Lewis P., Yih W., Riedel S. and Petroni F. Autoregressive search engines: generating substrings as document ide 阅读全文

摘要： 目录概Burrows-Wheler Transform (BWT)编码性质解码FM-index直观但简陋的方式更高效的方式代码 Langmead B. Burrows-Wheeler transform and FM Index. burning-BWT算法浅析-(一) 概 有趣的编解码. Burr 阅读全文

摘要： 目录概符号说明GraphPrompt代码 Liu Z., Yu X., Fang Y. and Zhang X. GraphPrompt: Unifying pre-training and downstream tasks for graph neural networks. WWW, 2023. 阅读全文

摘要： 目录概符号说明GPT-GNN代码 Hu Z., Dong Y., Wang K., Chang K. and Sun Y. GPT-GNN: Generative pre-training of graph neural networks. KDD, 2020. 概 比较早的一篇图预训练模型. 符号 阅读全文

摘要： 目录概主要内容代码 Jiang J., Zhou K., Zhao W. and Wen J. UniKGQA: Unified retrieval and reasoning for solving multi-hop question answering over knowledge graph 阅读全文

摘要： 目录概主要内容代码 Sun H., Dhingra B., Zaheer M., Mazaitis K., Salakhutdinov R. and Cohen W. W. Open domain question answering using early fusion of knowledge 阅读全文

摘要： 目录概符号说明Pixie Eksombatchai C., Jindal P., Liu J. Z., Liu Y., Sharma R., Sugnet C., Ulrich M. and Leskovec J. Pixie: A system for recommending 3+ billio 阅读全文

摘要： 目录概主要内容Metropolis graph sampling H\¨{u}bler C. and Kriegel H., Borgwardt K. and Ghahramani Z. Metropolis algorithms for representative subgraph sampli 阅读全文

摘要： 目录概主要内容 Leskovec J. and Faloutsos C. Sampling from large graphs. KDD, 2006. 概 讨论了不同稀疏化方法对于 large-graph 的`结构' 的保持. 主要内容 作者本文的目的是希望比较不同的'稀疏化'方法: 利用一些方法从 阅读全文

摘要： 目录概符号说明NeuralSparse Zheng C., Zong B., Cheng W., Song D., Ni J., Yu W., Chen H. and Wang W. Robust graph representation learning via neural sparsifica 阅读全文

摘要： 目录图的构建同质图异质图MessagePassing PyG 图的构建 同质图 假设我们要构建一个 graph \(\mathcal{G}=\langle \mathcal{V}, \mathcal{E} \rangle\), 其中 \(|\mathcal{V}| = V, |\mathcal{E} 阅读全文

摘要： 目录概TGINMotivation: Triangle 的重要性Model代码 Jiang W., Jiao Y., Wang Q., Liang C., Guo L., Zhang Y., Sun Z., Xiong Y. and Zhu Y. Triangle graph interest ne 阅读全文

摘要： 目录概DG-ENN Guo W., Su R., Tan R., Guo H., Zhang Y., Liu Z., Tang R. and He X. Dual graph enhanced embedding neural network for ctr prediction. KDD, 202 阅读全文

摘要： 目录概Fi-GNN代码 Li Z., Cui Z., Wu S., Zhang X. and Wang L. Fi-GNN: Modeling feature interactions via graph neural networks for ctr prediction. CIKM, 2019. 阅读全文

摘要： 目录概符号说明Graph-Laplacian for SSL Streicher O. and Gilboa G. Graph laplacian for semi-supervised learning. arXiv preprint arXiv:2301.04956, 2023. 概 标题取得有 阅读全文

摘要： 目录概符号说明WNLL Shi Z., Osher S. and Zhu W. Weighted nonlocal laplacian on interpolation from sparse data. 2017, J. Sci. Comput. 概 针对 graph laplacian 提出的一 阅读全文

摘要： 目录概符号说明SimRecPrediction-Level DistillationEmbedding-level DistillationAdaptive Contrastive Regularization总的损失代码 Xia L., Huang C., Shi J. and Xu Y. Gra 阅读全文

摘要： 目录概UnKD代码 Chen G., Chen J., Feng F., Zhou S. and He X. Unbiased knowledge distillation for recommendation. WSDM, 2023. 概 考虑流行度偏差的知识蒸馏, 应用于推荐系统. UnKD M 阅读全文

摘要： 目录概DIST代码 Huang T., You S., Wang F., Qian C. and Xu C. Knowledge distillation from a stronger teacher. NIPS, 2022. 概 用 Pearson correlation coefficient 阅读全文

摘要： 目录概DE-RRDDistillation Experts (DE)Relaxed Ranking Distillation (RRD)代码 Kang S., Hwang J., Kweon W. and Yu H. DE-RRD: A knowledge distillation framewor 阅读全文