馒头and花卷

摘要： 目录引线性量化 (Linear Quantization)对称量化非对称量化非线性量化Logarithmic QuantizationPower-of-XRoundingDeterministic roundingStochastic rounding [1] 进击的程序猿-模型压缩-神经网络量化基 阅读全文

摘要： 目录概AWQ代码 Lin J., Tang J., Tang H., Yang S., Chen W., Wang W., Xiao G., Dang X., Gan C. and Han S. AWQ: Activation-aware weight quantization for llm co 阅读全文

摘要： 目录概Lion代码 Chen X., Liang C., Huang D., Real E., Wang K., Liu Y., Pham H., Dong X., Luong T., Hsieh C., Lu Y. and Le Q. V. Symbolic discovery of optimi 阅读全文

摘要： 目录概Gradient Noise Scale McCandlish S., Kaplan J., Amodei D. and OpenAI Dota Team. An empirical model of large-batch training. 2018. 概 本文讨论了随着 batch si 阅读全文

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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

摘要： 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