Lie.Groups .Lie.Algebras and Representations Brian.C..Hall.

本文为读文章笔记，受所学所知限制，如有出错，恭请指正。 A Simple Framework for Contrastive Learning of Visual Representations 作者： Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton 本文提出一种简洁有效的设计的无监督设计，并且以7%的margin刷新了SOTA。 摘要直译：这篇文章提出了SimCLR， 一种简单的、用于视觉表征对比学习的框架。作者们简化了最近刚提出的对比自监督学习算法，并且不需要特别的架构或者J记忆库。为了探究是什么使得对比预测

This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging. Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.

参考https://distill.pub/2021/understanding-gnns，学习图的谱表征方法。

最近课程需要做论文Presentation，选了一篇2014年的，DeepWalk: Online Learning of Social Representations，有需要的可以下载

车载雷达的栅格地图表示

《Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries》文章matlab代码实现

这是关于概率统计的群表示的电子书，高清，最新版本，经典著作，英文版

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

Compressed-Sensing is a recent branch that separated from sparse and redundant representations, becoming a center of interest of its own. Exploiting sparse representation of signals, their sampling can be made far more eective compared to the classical Nyquist-Shannon sampling. In a recent work that emerged in 2006 by Emmanuel Candes, Justin Romberg, Terence Tao, David Donoho, and others that followed, the theory and practice of this field were beautifully formed, sweeping many researchers and practitioners in excitement. The impact this field has is immense, strengthened by the warm hug by information-theorists, leading mathematicians, and others. So popular has this field become that many confuse it with being the complete story of sparse representation modeling. In this book I discuss the branch of activity on Compressed-Sensing very briefly, and mostly so as to tie it to the more general results known in sparse representation theory. I believe that the accumulated knowledge on compressed-sensing could easily fill a separate book.