Auto-encoders
What Regularized Auto-Encoders Learn from the Data-Generating Distribution
... Regularized auto-encoders (see Bengio et ...An auto-encoder maps inputs x to an internal representation (or code) f (x) through the encoder function f, and then maps back f (x) to the input space ...
31
Data Integration Using Through Attentive Multi View Graph Auto Encoders
... chart auto-encoders, and include receptive process to ascertain the loads for every see regarding equivalent jobs and characteristics for greater ...
6
Estimating User Location in Social Media with Stacked Denoising Auto encoders
... denoising auto- encoders (SDA) and apply it to sentiment classifi- cation of Amazon ...ing auto-encoders can capture meaningful represen- tations from reviews and outperform state-of-the- art ...
10
Title: A New Distributed Denial-of-Service (DDoS) Attacks Detection System Combining Multistage Auto-Encoders with Radial Basis Function (RBF)
... multistage auto-encoders based on Radial Basis Function ...using auto- encoders and the number of auto-encoders depended on the data nonlinearity and ...first auto-encoder ...
6
Unsupervised Sentence Compression using Denoising Auto Encoders
... We present a fully unsupervised approach to the task of sentence compression in the form of a denoising auto-encoder with additive noising and word shuffling. Our model achieves com- parable scores in human ...
10
Segmenting Atrial Fibrosis from late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders
... In this study, we present a fully automatic framework for an efficient and objective atrial fibrosis assessment using: 1 a fully-automated multi-atlas based whole heart segmentation MA-W[r] ...
13
Unsupervised Latent Tree Induction with Deep Inside Outside Recursive Auto Encoders
... The inside- outside recursive neural network model for depen- dency parsing.. In Empirical Methods in Natural Language Processing EMNLP..[r] ...
13
Deep Recurrent Generative Decoder for Abstractive Text Summarization
... variational auto-encoders used in our framework have better representation ability and can capture more effective and complicated la- tent structures from the sequence ...
10
Representation decomposition for knowledge extraction and sharing using restricted Boltzmann machines
... RepDeepLearn: Implementation of representation/deep learning and reasoning models such as restricted Boltzmann machines RBMs, Auto Encoders, Non-negative Matrix Factorization, Sparsity, [r] ...
179
Insights into deep learning methods with application to cancer imaging
... the auto-encoder architecture for unsupervised learning, and explore similarities and differences with the supervised ...of auto-encoders by making connections with methods for dimensionality ...
74
Performance evaluation of deep feature learning for RGB-D image/video classification
... Denoising Auto-Encoders (SDAE), Con- volutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) Neural Networks), we conduct extensive experiments on five popular RGB- D datasets including three ...
49
DEVELOPMENT AND APPLICATION OF A STAGE GATE PROCESS TO REDUCE THE UNERLYING RISKS OF IT SERVICE PROJECTS
... Sparse Auto Encoders (SSAE) Deep Neural Network model and Bi-propagation Deep Neural Network algorithm separately for matching step, which are two more biologically inspired neural networks for learning the ...
13
Review of Deep Neural Network Based on Auto encoder
... automatic encoders, deep belief networks, and convolutional neural networks are ...on Auto-encoders is described, and the application of hybrid neural networks in various types is ...on ...
8
On Locally Invertible Encoders and Multidimensional Convolutional Codes
... Traditional trellis based approaches used for decoding 1-D convolutional codes do not generalize in a straightforward manner to higher dimensions. In the 1-D case, the Viterbi and BCJR algorithms exploit the natural ...
112
Extreme Learning Machines as Encoders for Sparse Reconstruction
... The underlying concept in all the above described techniques is that they solve the reconstruction inverse problem in a feature or basis space where the number of unknowns are comparable to the number of constraints ...
31
Context encoders as a simple but powerful extension of word2vec
... Based on an intuitive interpretation of the con- tinuous bag-of-words (CBOW) word2vec model’s negative sampling training objective, we propose an extension of the model we call context encoders (ConEc). This ...
5
Structural Neural Encoders for AMR to text Generation
... Abstract Meaning Representation (AMR; Ba- narescu et al. 2013) is a semantic graph represen- tation that abstracts away from the syntactic real- ization of a sentence, where nodes in the graph represent concepts and ...
10
Non-malleable Randomness Encoders and their Applications
... In this work, we introduce Non-malleable Randomness Encoders. Informally, NMREs allow for the generation of randomness r along with its correspond- ing non-malleable encoding C. The non-malleability is, as for ...
30
Memory Protection for Encoders in OLS Codes
... base on the properties of the code. However, when delays have to be low, parallel implementations are preferred. This is the case for OLS codes so as to be commonly used in high-speed applications. The reader is referred ...
7
The Use of Locally Invertible Convolutional Encoders for Encryption.
... convolutional encoders have been ...convolutional encoders for use as an encryption scheme, but Orumiechiha[20] refuted the security and presented an attack that took O(k 6 ) time to break ...invertible ...
75