Deep learning architectureSupervised learningSemi-supervised learningGraph-based embedding aims to reduce the dimension of high dimensional data and to extract relevant features for learning tasks. In this letter, we propose an Elastic graph-based embedding with deep architecture which deeply explores the...
引言(可跳过) 之前看了一篇[文章](Quantifying and Detecting Collective Motion in Crowd Scenes),里面用到流形学习去捕捉人群运动之间的拓扑关系。我emo了,然后就花时间看了流形学习,现在来总结一下。 降维 降维的本质,就是换个角度、坐标系、空间(欧式空间、黎曼几何)重新审视数据 下图是[大牛总结的](机器学习-...
machine-learningdeep-learningtensorflowvaemanifold-learningvariational-autoencodervon-mises-fisherhyperspherical-vae UpdatedDec 1, 2018 Python drewwilimitis/Manifold-Learning Star222 Code Issues Pull requests Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scali...
深度学习(Deep Learning)话题下的优秀答主75 人赞同了该文章 目录 收起 摘要 引言 方法 实验 总结 参考文献 摘要 华为诺亚实验室提出了一种适用于视觉Transformer的新型知识蒸馏方法,在ViT和Swin Transformer模型上的表现优于原始蒸馏方法和Facebook的DeiT。论文提出在细粒度特征构成的流形空间中进行知识蒸馏,旨在利...
manifold-learningrpcalatent-spacebiganvae-pytorch UpdatedMay 18, 2023 Jupyter Notebook elaaj/weisfeiler-and-manifold-techniques-svm Star0 Code Issues Pull requests The goal here is to use a graph kernel and a manifold learning technique in conjunction with Support Vector Machines to enhance the SVM...
Deep neural networks have become the main work horse for many tasks involving learning from data in a variety of applications in Science and Engineering. Traditionally, the input to these networks lie in a vector space and the operations employed within the network are well defined on vector-spac...
首先, manifold learning的一个基本假设是，数据在manifold上，而manifold上足够小的区域近似于tangent ...
In the proposed deep learning based framework, Manifold Regularized Convolutional Layers (MRCL) improve traditional convolutional layers by learning the relationship among outputs of neurons. Besides, in the proposed mapping relationship learning method, different modals of face representations are naturally ...
Resorting to the known physics equations provides explainability, error esti- mates in some cases, and good performance with sparse learning databases. Clas- sical and modern (e.g., deep learning) machine learning technologies are used to assist projection-based reduced-order models, either in ...
Learning meaningful representations using deep neural networks involves designing efficient training schemes and well-structured networks. Currently, the method of stochastic gradient descent that has a momentum with dropout is one of the most popular training protocols. Based on that, more advanced ...