From Theory to Applications in Signal and Image Processing

GTM042 Linear Representations of Finite Groups, Springer 1977.pdf

论文《汉语表达的深度学习需要分词吗？》

Understanding Deep Image Representations by Inverting Them.zip

Distributed Representations of Words and Phrases and their Compositionality.zip

本书主要描述了基于压缩感知的理论，恢复算法，还有详细介绍了应用，特别适合学习压缩感知的人使用

这是一本关于稀疏表达在信号和图像处理中运用的权威书籍《Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing》，也是第一部关于稀疏表达的书籍。是稀疏表达的大牛Michael Elad编写的。现在在卓越上有的卖，不过要7百多元，太贵，还是搞电子版看实惠点。

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. * Introduces theoretical and numerical foundations before tackling applications * Discusses how to use the proper model for various situations * Introduces sparse and redundant representations * Focuses on applications in signal and image processing The field of sparse and redundant representation modeling has gone through a major revolution in the past two decades. This started with a series of algorithms for approximating the sparsest solutions of linear systems of equations, later to be followed by surprising theoretical results that guarantee these algorithms’ performance. With these contributions in place, major barriers in making this model practical and applicable were removed, and sparsity and redundancy became central, leading to state-of-the-art results in various disciplines. One of the main beneficiaries of this progress is the field of image processing, where this model has been shown to lead to unprecedented performance in various applications. This book provides a comprehensive view of the topic of sparse and redundant representation modeling, and its use in signal and image processing. It offers a systematic and ordered exposure to the theoretical foundations of this data model, the numerical aspec

BP算法的经典文章。反向传播算法最早于上世纪70年代被提出，但是直到1986年，由David Rumelhart, Geoffrey Hinton, 和Ronald Williams联合发表了这篇论文之后，人们才完全认识到这个算法的重要性。

深度学习经典论文DeepWalk: Online Learning of Social Representations