Type or paste a DOI name into the text box. With new neural network architectures popping up every now and then, it’s applied Network Coding in Wireless Networks PDF to keep track of them all.
Författare: Johannes Hund.
Masterarbeit aus dem Jahr 2007 im Fachbereich Informatik – Internet, neue Technologien, Note: 1,3, Fachhochschule Heidelberg, Sprache: Deutsch, Abstract: Network coding describes a technique to perform coding operations on packet contents throughout the network, increasing the information density of a single transmission and therefore increasing network throughput.
This book describes the portation of an application of network coding onto an embedded linux. It therefore describes the software COPE, which is a network coding implementation based on the modular software router CLICK. This router is also introduced.
Then the book explains the embedded Linux system OpenWRT and the portation of a software onto that system as well as the compilation and deployment of a new firmware image.
Then the experiment and its results are explained.
The work then introduces novel schemes of network coding based on the idea behind COPE. These schemes are based on the improvement of resience rather than throughput.
So I decided to compose a cheat sheet containing many of those architectures. Most of these are neural networks, some are completely different beasts. One problem with drawing them as node maps: it doesn’t really show how they’re used. The use-cases for trained networks differ even more, because VAEs are generators, where you insert noise to get a new sample. It should be noted that while most of the abbreviations used are generally accepted, not all of them are. RNNs sometimes refer to recursive neural networks, but most of the time they refer to recurrent neural networks.
That’s not the end of it though, in many places you’ll find RNN used as placeholder for any recurrent architecture, including LSTMs, GRUs and even the bidirectional variants. Composing a complete list is practically impossible, as new architectures are invented all the time. Even if published it can still be quite challenging to find them even if you’re looking for them, or sometimes you just overlook some. For each of the architectures depicted in the picture, I wrote a very, very brief description. You may find some of these to be useful if you’re quite familiar with some architectures, but you aren’t familiar with a particular one. Neural networks are often described as having layers, where each layer consists of either input, hidden or output cells in parallel.
The perceptron: a probabilistic model for information storage and organization in the brain. FFNNs with radial basis functions as activation functions. Doesn’t mean they don’t have their uses, but most FFNNs with other activation functions don’t get their own name. This mostly has to do with inventing them at the right time.
Radial basis functions, multi-variable functional interpolation and adaptive networks. Each node is input before training, then hidden during training and output afterwards. The networks are trained by setting the value of the neurons to the desired pattern after which the weights can be computed. The weights do not change after this.