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scintillate    
v. 放出火花;闪烁

放出火花;闪烁

scintillate
v 1: give off; "the substance scintillated sparks and flashes"
2: reflect brightly; "Unquarried marble sparkled on the
hillside" [synonym: {sparkle}, {scintillate}, {coruscate}]
3: emit or reflect light in a flickering manner; "Does a
constellation twinkle more brightly than a single star?"
[synonym: {twinkle}, {winkle}, {scintillate}]
4: physics: fluoresce momentarily when struck by a charged
particle or high-energy photon; "the phosphor fluoresced"
5: be lively or brilliant or exhibit virtuosity; "The musical
performance sparkled"; "A scintillating conversation"; "his
playing coruscated throughout the concert hall" [synonym:
{sparkle}, {scintillate}, {coruscate}]

Scintillate \Scin"til*late\, v. i. [imp. & p. p. {Scintillated};
p. pr. & vb. n. {Scintillating}.] [L. scintillare,
scintillatum, from scintilla a spark. Cf. {Stencil}.]
1. To emit sparks, or fine igneous particles.
[1913 Webster]

As the electrical globe only scintillates when
rubbed against its cushion. --Sir W.
Scott.
[1913 Webster]

2. To sparkle, as the fixed stars.
[1913 Webster]


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英文字典中文字典相关资料:


  • Convolutional 2D knowledge graph embeddings | Proceedings of the Thirty . . .
    In this work we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets
  • Convolutional 2D Knowledge Graph Embeddings | Proceedings of the AAAI . . .
    In this work we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets
  • [1707. 01476] Convolutional 2D Knowledge Graph Embeddings
    In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets
  • ConvE: Convolutional 2D Knowledge Graph Embeddings - 知乎
    本文提出的正是应用于知识推理链路预测任务的学习模型——ConvE。 知识图谱可以包含百万数量级的事实三元组，因此为了应用到实际的生活场景上，链路预测器应该能以合理的方式扩展规模并且保持合适的参数量和计算复杂度。 为了解决大规模应用的问题，链路预测模型总是由简单的操作组成，如内积和嵌入空间上的矩阵乘法，以及使用有限的参数， DistMult 就是这样一种模型。 使用这种简单、浅、快的模型使得其可以扩展到大规模知识图谱上，但付出了模型表达力不足、学习特征能力不足的代价。 而在浅层模型中增加特征数量使得模型更具表达力的唯一方法就是增加embedding的维度，但是这样做又会使得其不能扩展到大规模知识图谱上，因为嵌入参数的总数与知识图谱中实体和关系的数量是成正比的。
  • Convolutional 2D Knowledge Graph Embeddings - Semantic Scholar
    This paper proposes the 1D and 2D convolutional combined embedding model CombE to relieve the problems of linear and nonlinear translation models, and shows that this method is superior to other recent methods
  • Convolutional 2D Knowledge Graph Embeddings - 百度学术
    In this work, we introduce ConvE, a multi-layer convolutional network model for link prediction, and report state-of-the-art results for several established datasets
  • (PDF) Convolutional 2D Knowledge Graph Embeddings - ResearchGate
    In this work, we introduce a convolutional neural network model, ConvE, for the task of link prediction where we apply 2D convolution directly on embeddings, thus inducing spatial structure
  • Convolutional 2D Knowledge Graph Embeddings resources.
    To run it on a new datasets, copy your dataset folder into the data folder and make sure your dataset split files have the name train txt, valid txt, and test txt which contain tab separated triples of a knowledge graph
  • convolutional 2D knowledge graph embedding 解读 - CSDN博客
    该博客介绍了如何利用2D卷积神经网络 (ConvE)解决大型知识图谱的链接预测任务，针对参数数量和计算效率的问题。 文章讨论了背景、动机，并详细阐述了模型设计，包括卷积操作、非线性函数的使用以及打分函数。
  • ConvE: Convolutional 2D Knowledge Graph Embeddings
    本文是论文 Convolutional 2D Knowledge Graph Embeddings 的阅读笔记和个人理解 与之前在 AcrE 中提到的ConvE不同, 本文重新对整篇论文进行叙述, 而非仅介绍论文中建模的部分 其实ConvE的出发点非常的简单, 就是之前的模型不够 深, 有些简单 因为之前使用的模型大多数采用矩阵映射, 内积等方式, 可能简单的模型能够处理小规模的KG, 想要提升性能就只能通过增大Embedding Dimension 它们在 大规模KG 上不一定能获得良好的效果 由于深度学习的兴起, 作者尝试将 卷积 引入到KGE领域, 使它能够在保证深度和模型复杂度的情况下能够处理大规模KG





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