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autoencoder

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dreasysnail/textCNN_public
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
tensorflow  convolutional  seq2seq  autoencoder  deep-learning  github 
8 days ago by nharbour
ymym3412/textcnn-conv-deconv-pytorch: text convolution-deconvolution auto-encoder model in PyTorch
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
pytorch  convolutional  seq2seq  autoencoder  deep-learning  github 
8 days ago by nharbour
Seq2Seq-PyTorch/nmt_autoencoder.py at master · MaximumEntropy/Seq2Seq-PyTorch
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
autoencoder  pytorch  nlp  lstm  deep-learning  seq2seq 
8 days ago by nharbour
[1801.01586] A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines
Many of the existing machine learning algorithms, both supervised and unsupervised, depend on the quality of the input characteristics to generate a good model. The amount of these variables is also important, since performance tends to decline as the input dimensionality increases, hence the interest in using feature fusion techniques, able to produce feature sets that are more compact and higher level. A plethora of procedures to fuse original variables for producing new ones has been developed in the past decades. The most basic ones use linear combinations of the original variables, such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), while others find manifold embeddings of lower dimensionality based on non-linear combinations, such as Isomap or LLE (Linear Locally Embedding) techniques.
More recently, autoencoders (AEs) have emerged as an alternative to manifold learning for conducting nonlinear feature fusion. Dozens of AE models have been proposed lately, each with its own specific traits. Although many of them can be used to generate reduced feature sets through the fusion of the original ones, there also AEs designed with other applications in mind.
The goal of this paper is to provide the reader with a broad view of what an AE is, how they are used for feature fusion, a taxonomy gathering a broad range of models, and how they relate to other classical techniques. In addition, a set of didactic guidelines on how to choose the proper AE for a given task is supplied, together with a discussion of the software tools available. Finally, two case studies illustrate the usage of AEs with datasets of handwritten digits and breast cancer.
tutorials  autoencoder  fusion 
march 2018 by arsyed

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