随笔分类 - 杂学
摘要：目录TPPEvolutionary point processesConditional intensity function [\(t\)]Conditional intensity function [\(t, \kappa\)]InferenceSimulationInverse Method
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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
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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
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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
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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
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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
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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
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摘要：目录概符号说明GTAM交替优化求解 Wang J., Jebara T. and Chang S. Graph transduction via alternating minimization. ICML, 2008. 概 一种对类别不均更鲁棒的半监督算法. 符号说明 \(\mathcal{X}_
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摘要：目录概符号说明图的构建Graph Sparsification\(\epsilon\)-neighborhood graph\(k\)NN graph\(b\)-MatchingGraph Edge Re-Weighting Jebara T., Wang J. and Chang S. Graph
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摘要：[TOC] > [1] [Unigram](http://arxiv.org/abs/1804.10959) > [2] [SentencePiece](https://colabdoge.medium.com/understanding-sentencepiece-under-standing-s
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摘要：[TOC] > [Kumar R., Vassilvitskii S. Generalized distances between rankings. WWW, 2010.](https://dl.acm.org/doi/10.1145/1772690.1772749) ## 概 有些时候, 我们会
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摘要：Spielman D. A. Spectral and Algebraic Graph Theory. 概 设计 Hypercube 的特征值和特征向量的证明着实有趣, 特此记录. Hypercube 对于两个加权图 $G = (V, E, v)$ 和 $H = (W, F, w)$ 而言, $G
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摘要：Xu Y., Zhao S., Song J., Stewart R. and Ermon S. A theory of usable information under computational constraints. International Conference on Learning
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摘要：Zhu D., Li G., Wang B., Wu X. and Yang T. When AUC meets DRO: Optimizing partial auc for deep learning with non-convex convergence guarantee. In Inter
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摘要：Bengio Y., L\acute{e}onard N. and Courville A. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv prepr
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摘要：Chen Y. Lecture 4: Importance Sampling and Rejection Sampling. Importance Sampling 设想我们希望估计这样的一个值: $$ I = \mathbb{E}_p(f(X)) = \int f(x)p(x) dx; $$ 但是
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摘要：Blanc G. and Rendle S. Adaptive sampled softmax with kernel based sampling. In International Conference on Machine Learning (ICML), 2018. 概 这儿 已经讨论了现在
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摘要：Bengio Y. and Sen\acute{e}cal J. S. Adaptive importance sampling to accelerate training of a neural probabilistic language model. IEEE Transactions on
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摘要：Huang N. and Villar S. A short tutorial on the weisfeiler-lehman test and its variants. arXiv preprint arXiv:2201.07083, 2022. 概 本文介绍了 WL-Test 和它的一些变体
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摘要：Poole B., Ozair S., van den Oord A., Alemi A. A. and Tucker G. On variational bounds of mutual information. In International Conference on Machine Lea
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