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  • An Introduction to Variational Autoencoders.pdf

    Variational autoencoders provide a principled framework for learning deep latent-variable models and corresponding inference models. In this work, we provide an introduction to variational autoencoders and some important extensions.

    2019-09-21
    5
  • Optimal Transport for Applied Mathematicians.pdf

    This book contains a rigorous description of the theory of optimal transport and of some neglected variants and explains the most important connections that it has with many topics in evolution PDEs, image processing, and economics.学习最优化传输的入门书籍

    2019-09-21
    5
  • boosting for transfer learning

    boosting for transfer learning 的Python代码实现文件中的readmd有详细解释

    2018-12-20
    8
  • Optimal Transport for Domain Adaptation

    Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.

    2018-12-20
    8
  • 105.Dynamic Programming

    a good textbook for dynamic programming and describe a lot of useful method

    2018-11-02
    0
  • Supervised Sequence Labelling with Recurrent Neural Networks

    a good source for learning recurrent neural network

    2018-11-02
    2
  • MachineLearning-master

    包含 感知机 KNN 决策树 以及逻辑回归等算法 手动搭建

    2018-10-19
    0
  • Analysis of Representations for Domain Adaptation

    迁移学习中的经典理论分析 对深入理解迁移学习算法很有帮助

    2018-10-19
    5
  • Self-taught Learning Transfer Learning from Unlabeled Data

    迁移学习领域中的经典论文 自监督学习在无标签数据中的应用

    2018-10-19
    2
  • 麻省理工机器学习笔记

    麻省理工机器学习的教程对入门者十分友好 介绍了感知机 支持向量机等各种算法

    2018-10-19
    10
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