1 Preliminaries 3 1.1 A Bit of History 4 1.2 Introduction 7 1.3 Motivation 8 1.3.1 Optics 8 1.3.2 Shape of a Liquid Drop 10 1.3.3 Optimization of a River-Crossing Trajectory 12 1.3.4 Summary 14 1.4 Extrema of Functions 14 1.5 Constrained Extrema and
This book presents the state of the art in sparse and multiscale image and signal processing, covering linear multiscale transforms, such as wavelet, ridgelet, or curvelet transforms, and non-linear multiscale transforms based on the median and math
新论文：最近6个月以内的 Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models, S. Ioffe. Wasserstein GAN, M. Arjovsky et al. Understanding deep learning requires rethinking generalization, C. Zhang et al. [pdf] 老论文：2012年以前的 An
Deep learning .................................................. 417 10.1 Deep Feedforward Networks ...................................................420 The MNIST Evaluation ........................................................... 421 Losses an
Deep Learning: Practical Neural Networks with Java by Yusuke Sugomori English | 8 Jun. 2017 | ASIN: B071GC77N9 | 1057 Pages | AZW3 | 20.28 MB Build and run intelligent applications by leveraging key Java machine learning libraries About This Book De
深度学习工具包 Deprecation notice. ----- This toolbox is outdated and no longer maintained. There are much better tools available for deep learning than this toolbox, e.g. [Theano](http://deeplearning.net/software/theano/), [torch](http://torch.ch/) or [te
In order to denoise Poisson count data, we introduce a variance stabilizing transform (VST) applied on a filtered discrete Poisson process, yielding a near Gaussian process with asymptotic constant variance. This new transform, which can be deemed a
% u = TVDENOISE(f,lambda) denoises the input image f. The smaller % the parameter lambda, the stronger the denoising. % % The output u approximately minimizes the Rudin-Osher-Fatemi (ROF) % denoising model % % Min TV(u) + lambda/2 || f - u ||^2_2, %
一维滤波，小波去噪算法，包含多种小波去噪去噪，强制去噪、软阈值去噪、硬阈值去噪 (Wavelet denoising algorithm that contains a variety of wavelet denoising denoising forced denoising, soft thresholding and hard threshold denoising )
In the models that people obtain, there is often having noise. The presence of noise not only confuses the features and details on the surface of the model, but more importantly destroys the usability of the model. So removing the noise in the model
