wdqkdzz的博客

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.

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

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

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.

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

a good source for learning recurrent neural network

包含 感知机 KNN 决策树 以及逻辑回归等算法 手动搭建

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

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

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

python语言不错的资源结合大量实例有助于边学边做 包含游戏开发以及人工智能的各个方面

不错的入门书籍 让机器具备智能是人们长期追求的目标，但是关于智能的定义也十分模糊。Alan Tur- ing在1950年提出了著名的图灵测试：“一个人在不接触对方的情况下，通过一种特殊的 方式，和对方进行一系列的问答。如果在相当长时间内，他无法根据这些问题判断对方 是人还是计算机，那么就可以认为这个计算机是智能的