For this\npurpose, we employed the normalized volume element of the Bures (minimal\nmonotone) metric as a probability distribution over the fifteen-dimensional\nconvex set of 4 x 4 density matrices. Here, we provide further/independent\n(quasi-Monte Carlo numerical integration) evidence of a ...
How many miles to $\\beta\\omega$? -- Approximating $\\beta\\omega$ by metric-dependent compactifications 来自 arXiv.org 喜欢 0 阅读量: 63 作者:M Kada,K Tomoyasu,Y Yoshinobu 摘要: It is known that the Stone-\v{C}ech compactification $\stonecech{X}$ of a non-compact metrizable ...
TADAM:Task dependent adaptive metric for improved few-shot learning,程序员大本营,技术文章内容聚合第一站。
TADAM: Task dependent adaptive metric for improved few-shot learningarxiv.org/abs/1805.10123 这篇paper主要关注了分类任务中,同一任务的多个类别之间有较强的相关性,从而根据任务中多个类别的共同特性对特征提取器进行调整。也就是小样本学习在图像识别领域的课题,目标在提高检测精度上。另外,文中也对常用的两...
An entropy formula for the heat equa- tion on manifolds with time-dependent metric, application to ancient solutions, preprint.Guo, H., Philipowski, R. and Thalmaier, A.: An entropy formula for the heat equation on manifolds with time-dependent metric, application to ancient solutions. ...
A locally weighted normalized mutual information (wNMI) metric is proposed to highlight spatial correspondences in an image pair. Aiming to account for local similarities, our proposal computes the...doi:10.1007/978-3-319-19390-8_34M. Orbes-Arteaga...
In this paper, an alternative metric addressing these drawbacks is introduced. The metric makes use of pre-characterized load dependent component models and estimates efficiency for arbitrary input data. The results are objectively comparable between different data center configurations as well as between...
This paper approaches the speaker recognition domain, providing a supervised text-dependent voice recognition approach. First, we propose a delta delta mel cepstral based vocal feature extraction method. Next, we provide a special nonlinear metric, derived from the Hausdorff distance for sets. The met...
metric度量中,欧式距离效果好于余弦距离 1.尺度scale the distance metric by a learnable temperatureα 交叉熵损失: 最小化同类别的距离,最大化非同类别的距离。相当于只学习hard sample 2. task conditioning feature extractorfϕ(·)to be task-dependentfϕ(X,Γ)...
Metric Scaling: 这是第一个提出度量缩放来提高小样本算法的性能的研究。 Task Conditioning: 我们使用任务编码网络来提取基于任务样本集的任务表示。这用于通过FILM[19]影响特征提取器的行为。 Auxiliary task co-training: 在传统的监督分类任务上共同训练特征提取降低了训练复杂性,并提供了更好的泛化。