networks guided reading

[Reading Notes] iOS-deep anatomy of peer-to-peer networks, Reading Notes ios- The Protocol itself is a custom protocol running on UDP. I decided to use a custom protocol as simple as that. First, the current task seems simple enough, so it is easier to build a custom protocol directly than to try to improve a current protocol. Second, custom protocols can minimiz

(MHD)Average Surface Distance (ASD).Private Summary (this summary does not have universality, give yourself to see ... do not like to spray):There is no particularly significant innovation, but there is at least a place for reference.First of all, a bit of a question: 3D segmentation has always been a problem, can be trained less data, and the method used in the text is very small, although the overlap cut (not specified the size of the original T1 size and overlap), but the data extension is n

Internet network layer, Therefore, the convergence of underlying Iot heterogeneous networks and the Internet based on the IP protocol will be the main development direction of wireless sensor networks in the future. At the network layer, future research focuses on the seamless connection between the wireless sensor network (WSN) and the Internet, routing security, and routing service quality assurance. Ap

be used to prevent overfitting when training data is lowDisadvantage: The training time will be extended, but does not affect the test timesome MATLAB functionsUse RNG in 1.matlab to replace the popular interpretation of rand (' seed ', SD), Randn (' seed ', SD) and rand (' state ', SD)ExperimentWhat I did was experiment was repeated deep learning: 41 (Dropout simple comprehension) in the experiment, the result is the same, specifically to see the blog postReference documents:Dropout:a simple-t

ICCV2017 paper "unpaired image-to-image translation using cycle-consistent adversarial Networks" Reading notes Title: unpaired image-to-image translation using cycle-consistent adversarial Networks Author: Jun-yan Zhu, Taesung Park, etc., Berkeley AI (BAIR) Laboratory project Address: https://junyanz.github.io/CycleGAN/ Brief IntroductionThis article is propos

upper part), It will output a coarse feature map.The advantage of this is that you can take good advantage of the trained supervised pre-training network, do not like the existing methods, from beginning to end training, only need to fine-tuning, training efficient.(2) Add In-network upsampling layer.The feature map obtained in the middle is sampled on bilinear, which is the deconvolution layer. The implementation of the conv of the forward and reverse transfer process can be reversed.2) How to

Bishi finally came to an end, the traditional method of vision to do my whole people are very run, finally ended, you can see some of the papers on hold for a long time, boingonium hinderingDensely Connected convolutional Networks actually came out early, CVPR best paperThink before reading the paper, or the entire network structure of dense net to Http://ethereon.github.io/netscope/#/editor above the visua

demerits of the feature selection. Therefore, the majority of human resources are invested in the development and screening of features, not only need to have a deep understanding of the task problem areas, but also spend a lot of time repeatedly experimenting, which also limits the effect of shallow model.In fact, the layered initialization of the deep model can also be seen as a feature learning process, through the hidden layer of the original input step by step abstract representation, to l

I. Documentation names and authorsconvolutional neural Networks at Constrained time COST,CVPR two. Reading timeJune 30, 2015Three. Purpose of the documentThe author hopes to improve the accuracy of CNN by modifying the model depth and the parameters of the convolution template, while maintaining the computational complexity. Through a lot of experiments, the author finds the importance of different paramete

place. This on-sample kernel (parameter) can be learned in FCN, and the bilinear interpolation can be chosen when initializing; The resulting text block why not directly as a text line, why do you have to do a separate step to create text line? First, when more than one text line is close, it is easy to be contained by a block; second, the text block gets a range that is too coarse and has no exact text position; How does this projection algorithm for

1. Introduction:YouTube's recommended challenges:Scale: Many algorithms are useful in small data, which is useless on YouTube;Freshness: Need to be sensitive to the new uploaded video;Noisy: no real user feedback; lack of structured data2. Skip3. Candidate Generation:The previous model was based on matrix decomposition; The first layers of the YouTube DL model are the use of neural networks to simulate this decomposition, which can be seen as a nonlin

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

input text. The text is stored in the next available memory slots in its original Formthe G module are thus only used to store this new Memory, so-old memories is not updated.Watermark/2/text/ahr0cdovl2jsb2cuy3nkbi5uzxqv/font/5a6l5l2t/fontsize/400/fill/i0jbqkfcma==/dissolve/70/gravity /center ">Thirdly, the scheme in the paper solves this problem. How far has it been resolved?In fact, the main paper is to mention a large framework, and the framework of each module is able to change, so that can