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目录概UoT代码 Hu Z., Liu C., Feng X., Zhao Y., Ng S., Luu A. T., He J., Koh P. W. and Hooi B. Uncertainty of thoughts: Uncertainty-aware planning enhances 阅读全文
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目录概滑动窗口上的快速算法 Farhang-Boroujeny B. and Gazor S. Generalized sliding fft and its application to implementation of block lms adaptive filters. TSP, 1994 阅读全文
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目录概Frequent DirectionsFrequent Directions over Slidding Windows代码 Ghashami M., Liberty E., Phillips J. M. and Woodruff D. P. Frequent directions : Sim 阅读全文
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目录概Graph Diffusion Equations 的传统近似解法Sequential local updates via Successive Overrelaxation (SOR)代码 Bai J., Zhou B., Yang D. and Xiao Y. Faster Local S 阅读全文
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目录概符号说明STARRetrievalRanking最后的结果 Lee D., Kraft A., Jin L., Mehta N., Xu T., Hong L., Chi E. H. and Yi X. STAR: A simple training-free approach for rec 阅读全文
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目录概Graph CLSimGCL代码 Yu J., Yin H., Xia X., Chen T., Cui L. and Huang N. Q. V. Are graph augmentations necessary? simple graph contrastive learning for 阅读全文
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目录概主要内容代码 Wu Y., Zhang L., Mo F., Zhu T., Ma W. and Nie J. Unifying graph convolution and contrastive learning in collaborative filtering. KDD, 2024. 阅读全文
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目录概主要内容原文代码 Tan Z., Zhang Y., Yang J. and Yuan Y. Contrastive learning is spectral clustering on similarity graph. ICLR, 2024. 概 本文将对比学习与谱聚类联系在一起. 主要内 阅读全文
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目录概AuxiLearn问题设定理解两阶段的训练代码 Navon A., Achituve I., Maron H., Chechik G. and Fetaya E. Auxiliary learning by implicit differentiation. ICLR, 2021. 概 通过 阅读全文
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目录概Decoupled Attention and Representation Embeddings (DARE) model Feng N., Pang J., Wu J., Chen B., Wang X., Li Q., Hu X., Jiang J. and Long M. Long-s 阅读全文
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目录概符号说明ModularityAgglomerative Hierarchical ClusteringLouvainModularity-based Graph ClusteringRabbit代码 [1] Newman M. E. J. and GirvanM. Finding and ev 阅读全文
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目录概符号说明AdafactorFactored Second Moment EstimationNo MomentumOut-of-Date Second Moment Estimator算法代码 Shazeer N. and Stern M. Adafactor: Adaptive learni 阅读全文
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目录概符号说明SM3区间的划分代码 Anil R., Gupta V., Koren T., Singer Y. Memory-efficient adaptive optimization. NeurIPS, 2019. 概 本文提出了一种 memory-efficient 的优化器: SM3. 阅读全文
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目录概METISCoarseningPartitioning phaseUncoarsening phase Karypis G. and Kumar V. A fast and high quality multilevel scheme for partitioning irregular gr 阅读全文
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目录概符号说明Vertex vs Edge partitioningNE (Neighbor Expansion)代码 Zhang C., Wei F., Liu Q., Tang Z. G. and Li Z. Graph edge partitioning via neighborhood he 阅读全文
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目录概DCN-v2 Wang R., Shivanna R., Cheng D. Z., Jain S., Lin D., Hong L. and Chi E. D. DCN V2: Improved deep & cross network and practical lessons for we 阅读全文
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目录概Adam-mini代码 Zhang Y., Chen C., Li Z., Ding T., Wu C., Ye Y., Luo Z. and Sun R. Adam-mini: Use fewer learning rates to gain more. arXiv preprint, 20 阅读全文
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目录概符号说明GaLore Zhao J., Zhang Z., Chen B., Wang Z., Anandkumar A. and Tian Y. GaLore: Memory-efficient llm training by gradient low-rank projection. IC 阅读全文
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目录概BAdam代码 Luo Q., Yu H. and Li X. BAdam: A memory efficient full parameter optimization method for large language models. arXiv preprint, 2024. 概 本文介 阅读全文
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目录概符号说明所有参数的 Hessian 矩阵Block-wise Hessian代码 Zhang Y., Chen C., Ding T., Li Z., Sun R. and Luo Z. Why transformers need adam: a hessian perspective. ar 阅读全文