To address this problem it is important to know how users interact with the system . This work constitutes a methodological contribution capable of identifying the context of use in which users perform interactions with a groupware application ( synchronous or asynchronous ) and provides , using machine learning techniques , generative models of how users behave . Additionally , these models are transformed into a text that describes in natural language the main characteristics of the interaction of the users with the system . The multimedia event is directly related to a series of works by Boetti featuring a grid-filling algorithm which has been implemented by the artist on a huge variety of planar supports through many collective projects . In this sense , the event is intended to be another possible realization of the original work exploiting a multimodal support , i.e. including sound ( music and audio processing ) and emphasizing the generative process defined by Boetti 's algorithm through interactivity . An analysis of a corpus of 50 realized Boetti 's works - all based on the same algorithm - has been conducted in order to define meaningful mapping strategies from the visual domain to music and audio-video processing . When the MAR assumption is incorrect , inferences are biased and predictive performance can suffer . Therefore , we model both the generative process for the data and the missing data mechanism . By learning these two models jointly we obtain improved performance over state-of-the-art methods when predicting the ratings and when modeling the data observation process . Here , we instead view adversarial attacks as a generative modelling problem , with the goal of producing entire distributions of adversarial examples given an unperturbed input . We show that this generative perspective can be used to design a unified encoder-decoder framework , which is domain-agnostic in that the same framework can be employed to attack different domains with minimal modification . Across three diverse domains --- images , text , and graphs --- our approach generates whitebox attacks with success rates that are competitive with or superior to existing approaches , with a new state-of-the-art achieved in the graph domain . This may be because the more common use of the subjunctive in English is captured by non-subjunctive structures like infinitival or null CP [ + indicative ] structures . From a generative perspective , the present study investigates second language ( L2 ) syntactic and semantic development of the Spanish subjunctive at the University level . The results of the study reveal that learners at all levels of instruction continued to transfer first language ( L1 ) syntactic rules of English irrealis to L2 structures during acquisition . During the last decades , a plethora of different and well-adapted models has been developed , but only little attention has been payed to the development of different and similarly well-adapted learning principles . Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications , too . ResultsHere , we propose a generalization of generative and discriminative learning principles containing the maximum likelihood , maximum a posteriori , maximum conditional likelihood , maximum supervised posterior , generative-discriminative trade-off , and penalized generative-discriminative trade-off learning principles as special cases , and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites . : subordinate clause , SUBJ : subjunctive , Notations : - : affix boundary , = : clitic boundary 1 . Theoretical approaches to cleft construction Thus far , different approaches have been adopted in order to reveal the true nature of cleft construction among which generative ones enjoy more popularity . These studies fall broadly into two categories : extrapositional and expletive approaches . In recent years , deep discriminative models have achieved extraordinary performance on supervised learning tasks , significantly outperforming their generative counterparts . However , their success relies on the presence of a large amount of labeled data . Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot learning setting to upper-bound generalisation errors on unseen tasks and samples . We also propose a generative-based approach to model the shared prior and the posterior of task-specific model parameters more expressively compared to the usual diagonal Gaussian assumption . We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate , with state-of-the-art calibration and classification results on few-shot classification ( mini-ImageNet and tiered-ImageNet ) and regression ( multi-modal task-distribution regression ) benchmarks . In this paper , we propose a novel technique to recognize faces across age progressions . We also analyze different best in class systems accessible in discriminative-based methodology and generative-based methodology ; this analyzes are performed on different standard age varying face databases . AbstractMetrics specifying distances between data points can be learned in a discriminative manner or fromgenerative models . In this paper , we show how to unify generative and discriminative learning of met-rics via a kernel learning framework . It is important to clarify the concept of system . Although the systems theory has shown the generality of the concept of system , it has not gone deep enough to discover the generative nature of systems . One of the most successful models is the generative adversarial network ( GAN ) gan , which employs a two player min - max game . The generative model , , samples the prior and generates the sample . The discriminator , , is trained to identify whether a point is a sample from the data distribution or a sample from the generative model . This non - linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research . We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature . Factoring out undesired variations from the data can be easily formulated as a general probabilistic model which admits two distinct ( independent ) “ sources ” ; an observed variable , which denotes the variations that we want to remove , and a continuous latent variable which models all the remaining information . This generative process can be formally defined as : where is an appropriate probability distribution for the data we are modelling . With this formulation we explicitly encode a notion of ‘ invariance ’ in our model , since the latent representation is marginally independent of the factors of variation . Recently , by combining with policy gradient , Generative Adversarial Nets ( GAN ) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation . However , the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process . As such , it limits its success when the length of the generated text samples is long ( more than 20 words ) . We see that these results are generally in line with the RG and CS metrics , with the Conditional Copy model having the highest number of supporting facts , and the reconstruction terms significantly improving the Joint Copy models . Using a Tukey HSD post - hoc analysis of an ANOVA with the number of contradicting facts as the dependent variable and the generating model and rater i d as independent variables , we found significant ( p < 0.01 ) pairwise differences in contradictory facts between the gold generations and all models except " Copy + Rec + TVD , " as well as a significant difference between " Copy + Rec + TVD " and " Copy " . We similarly found a significant pairwise difference between " Copy + Rec + TVD " and " Copy " for number of supporting facts . We propose a framework that combines an encoder and a generator to learn disentangled representations which encode meaningful information about the data distribution without the need for any labels . While current approaches focus mostly on the generative aspects of GANs , our framework can be used to perform inference on both real and generated data points . Experiments on several data sets show that the encoder learns interpretable , disentangled representations which encode descriptive properties and can be used to sample images that exhibit specific characteristics . 0 We study the problem of transferring a sample in one domain to an analog sample in another domain . Given two related domains , and , we would like to learn a generative function that maps an input sample from to the domain , such that the output of a given function , which accepts inputs in either domains , would remain unchanged . Other than the function , the training data is unsupervised and consist of a set of samples from each domain . section : Experiments We present results of our two models both on parsing ( discriminative and generative ) and as a language model ( generative only ) in English and Chinese . paragraph : Data . Firstly , the salient regions of a down image were cut from the image using the visual saliency model . Then , these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters , which accord with the statistical characteristics of dataset . At last , a DCNN with Inception module and its variants was constructed . The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals . The results indicate that the autoencoder with nonnegativity constraint ( ANC ) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder . The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel . This paper tests the hypothesis that information contained in ensemble activity recorded from the primate motor cortex about limb movements is preserved when the activity is projected onto a sparse basis . Spiking rate data from neurons in the motor cortex of an awake behaving macaque monkey was compressed using a sparse autoencoder network , and classifications of movement directions were made in the compressed space . Classifier performance is shown to be similar when using either compressed ( sparsened ) or uncompressed neural activity , demonstrating the potential use of the sparse autoencoder as an unsupervised compression algorithm for low power/low bandwidth wireless transmission of neural ensemble data . This paper studies the interturn short circuit fault identification method of permanent magnet synchronous motor , and proposes an efficient and accurate interturn short circuit fault identification method based on Generative Adversarial Networks and Sparse Autoencoder Network deep learning . In this method , negative sequence current and torque characteristic signals of permanent magnet synchronous motor are collected , sample data are expanded by means of Generative Adversarial Networks and network , a variety of training sets are constructed , and efficient and accurate fault feature classification and recognition are realized by combining with Sparse Autoencoder Network . The test results show that the accuracy of this method is up to ninety-nine percent . ABSTRACT In this article , we propose a novel approach based on convolutional features and sparse autoencoder ( AE ) for scene-level land-use ( LU ) classification . This approach starts by generating an initial feature representation of the scenes under analysis from a deep convolutional neural network ( CNN ) pre-learned on a large amount of labelled data from an auxiliary domain . In this paper , a novel deep learning framework for underwater target classification is proposed . First , instead of extracting features relying on expert knowledge , sparse autoencoder ( AE ) is utilized to learn invariant features from the spectral data of underwater targets . Second , stacked autoencoder ( SAE ) is used to get high-level features as a deep learning method . The IGMRF prior captures the smoothness as well as preserves sharp discontinuities and the sparsity prior captures the sparseness in the disparity map . We present a sparse autoencoder based approach for learning and inferring the sparse representation of disparities . An iterative two phase algorithm is proposed to solve our energy minimization problem . In order to perform the regularization , we propose to use the inhomogeneous Gaussian Markov random field ( IGMRF ) and sparsity priors in our energy function . A sparse autoencoder-based approach is proposed for learning and inferring the sparse representation of disparities . The IGMRF prior captures the smoothness as well as preserves sharp discontinuities while the sparsity prior captures the sparseness in the disparity map . This paper proposes a comprehensive computer-aided diagnosis ( CAD ) scheme for identifying the presence of malaria parasites in thick blood smear images . The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder . The optimum size of the CAD scheme used in this research is 12500 - 2500 - 100 - 50 - 2 , where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes . It can serve as a preprocessing step in the pipeline of automated understanding of the semantics of human speech . The paper proposes a Sparse Autoencoder based architecture for Punjabi and English spoken language Classification . A number of shallow architectures namely Soft-max classifier , SVM and deep architectures namely Artificial Neural Networks , SVM with Sparse Auto encoder and Softmax with sparse auto encoder ( with and without fine tuning ) have been compared on the same task . Fourier transform , principle component analysis , and t-distributed stochastic neighbor embedding methods are applied to visualize the data set , which proves the feasibility of classifying the PD current waveform . Two basic parts of this DL framework are sparse autoencoder layer and softmax layer , the former extracting features of the input signal and the latter operating as the classifier . Hyper-parameters of the network such as sparsity , activation function , number of hidden nodes , and network depth were discussed . An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results . However , this result can be improved by applying a sparse autoencoder learning algorithm to obtain a more distinguishable diffused image . Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented . A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size $ $ 2048\times 2048$$2048 × 2048 . We applied this model for feature extraction from pathological images of lung adenocarcinoma , which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network . The present paper aims to propose a new neural network called “ sparse semi-autoencoder ” to overcome the vanishing information problem inherent to multi-layered neural networks . The vanishing information problem represents a natural tendency of multi-layered neural networks to lose information in input patterns as well as training errors , including also natural reduction in information due to constraints such as sparse regularization . Good initialization is essential for training deep models . Hinton and Salakhutdinov adopted the layer - by - layer pretraining strategy to train a deep autoencoder . Krizhevsky initialized the weight of each layer by drawing samples from a Gaussian distribution with zero mean and 0.01 standard deviation . Among these methods , does this in a “ deep ” way by the layerwise training of a sequence of deep autoencoders , while gradually replacing source - domain samples with target - domain samples . This improves over a similar approach of that simply trains a single deep autoencoder for both domains . In both approaches , the actual classifier / predictor is learned in a separate step using the feature representation learned by autoencoder ( s ) . In contrast to , our approach performs feature learning , domain adaptation and classifier learning jointly , in a unified architecture , and using a single learning algorithm ( backpropagation ) . In this paper , we proposed to use the GAN framework as a variational inference algorithm for both discrete and continuous latent variables in probabilistic autoencoders . Our method called the adversarial autoencoder ( AAE ) , is a generative autoencoder that achieves competitive test likelihoods on real - valued MNIST and Toronto Face datasets . We discussed how this method can be extended to semi - supervised scenarios and showed that it achieves competitive semi - supervised classification performance on MNIST and SVHN datasets . document : PixelGAN Autoencoders In this paper , we describe the ‘ ‘ PixelGAN autoencoder ’ ’ , a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels ( PixelCNN ) that is conditioned on a latent code , and the recognition path uses a generative adversarial network ( GAN ) to impose a prior distribution on the latent code . We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder . Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization . This so - called Augmented Autoencoder has several advantages over existing methods : It does not require real , pose - annotated training data , generalizes to various test sensors and inherently handles object and view symmetries . Instead of learning an explicit mapping from input images to object poses , it provides an implicit representation of object orientations defined by samples in a latent space . Therefore , models capable of exploiting topological structures of graphs have been shown to achieve superior predictive performances on many LPNC tasks . We present a novel densely connected autoencoder architecture capable of learning a shared representation of latent node embeddings from both local graph topology and available explicit node features for LPNC . The resulting autoencoder models are useful for many applications across multiple domains , including analysis of metabolic networks for drug - target interaction , bibliographic networks , social networks such as Facebook ( For the reconstruction task , our LoNGAE model achieves superior precision@ performance when compared to the related SDNE model . Although both models leverage a deep autoencoder architecture for graph representation learning , the SDNE model lacks several key implementations necessary for enhanced representation capacity , namely parameter sharing between the encoder - decoder parts and end - to - end training of deep architectures . For link prediction , we observe that combining available node features always produces a significant boost in predictive performance . Zou et al . [ 10 ] learn invariant features from video by enforcing a temporal slowness constraint on the feature representation learned by a linear autoencoder . Sohn and Lee [ 11 ] and Hui We now describe our work from the perspective of learning autoencoders . Maximum - likelihood estimation in a regular autoencoder takes the following form : There are two key distinctions of note : ( 1 ) The autoencoder ( and denoising autoencoder ) effectively optimizes the first term in the vae objective ( Eq . 5 and Eq . 6 ) using a delta variational distribution section : Conclusion We introduce the Variational Fair Autoencoder ( VFAE ) , an extension of the semi - supervised variational autoencoder in order to learn representations that are explicitly invariant with respect to some known aspect of a dataset while retaining as much remaining information as possible . We further use a Maximum Mean Discrepancy regularizer in order to further promote invariance in the posterior distribution over latent variables . Technically , we present a novel adaptive variational AutoEncoder with a convolutional neural network . In the training , this AutoEncoder analyzes publicly available HRTFs dataset and identifies factors that depend on the individuality of users in a nonlinear space . In calibration , the AutoEncoder generates high-quality HRTFs fitted to a specific user by blending the factors . This problem has received increasing attention from researchers and practitioners recently . Among many approaches developed for detecting and preventing the abnormal accesses to information systems , Shrink AutoEncoder ( SAE ) is an appealing technique due to its simplicity in implementation and effectiveness in detecting network attacks . However , this model has a potential drawback when applying to datasets with the presence of several clusters . The reason is that it attempts to compress all normal data samples into a single cluster in the hidden space of an AutoEncoder . In our research , we introduce a hybrid model between K-means clustering algorithm and SAE to lessen the limitation of SAE in handling such datasets . This paper proposes a novel Learning Classifier System ( LCS ) which integrates Deep AutoEncoder named DAE to solve high-dimensional problems . In the proposed LCS , DAE starts to compress ( encode ) an environmental input as a high-dimensional information to an input of LCS as a low-dimensional information and decompresses ( decodes ) an output of LCS as a low-dimensional information to a system output as a high-dimensional information . For unimodal enhancement task , we indicate that the best recognition accuracy of 82.11 % on SEED dataset is achieved with shared representations generated by Deep AutoEncoder ( DAE ) model . For multimodal facilitation tasks , we demonstrate that the Bimodal Deep AutoEncoder ( BDAE ) achieves the mean accuracies of 91.01 % and 83.25 % on SEED and DEAP datasets , respectively , which are much superior to the state-of-the-art approaches . For cross-modal learning task , our experimental results demonstrate that the mean accuracy of 66.34 % is achieved on SEED dataset through shared representations generated by EEG-based DAE as training samples and shared representations generated by eye-based DAE as testing sample , and vice versa . Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data . In this paper , we propose a novel AutoEncoder Feature Selector ( AEFS ) for unsupervised feature selection which combines autoencoder regression and group lasso tasks . Compared to traditional feature selection methods , AEFS can select the most important features by excavating both linear and nonlinear information among features , which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions . Feature selection which select a subset of features from original ones has been proven to be effective and efficient to deal with high-dimensional data . In this paper , we propose a novel AutoEncoder Feature Selector ( AEFS ) for unsupervised feature selection . AEFS is based on the autoencoder and the group lasso regularization . Most existing methods use hand-crafted features in local spatial regions to identify anomalies . In this paper , we propose a novel model called Spatio-Temporal AutoEncoder ( ST AutoEncoder or STAE ) , which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions . In addition to the reconstruction loss used in existing typical autoencoders , we introduce a weight-decreasing prediction loss for generating future frames , which enhances the motion feature learning in videos . To deal with it , in this paper , we propose a Neural Network based CCRF ( NN-CCRF ) model that formulates CCRF into an end-to-end neural network framework , which could reduce the complexity in model training and improve the overall performance . We integrate CCRF with NN by introducing a Long Short-Term Memory ( LSTM ) component to learn the non-linear mapping from inputs to outputs of each region , and a modified Stacked Denoising AutoEncoder ( SDAE ) component for pairwise interactions modeling between regions . Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods . Based on hydrophobicity or hydrophilicity and their role in proteins ' second structure , they are divided into 6 groups , each assigned with scores . The data are prepared in the acceptable form to the automated prediction mechanisms , Probabilistic Neural Network ( PNN ) and Deep Neural NetworkStacked AutoEncoder ( DNN ) . With Jackknife cross validation we show that the prediction accuracy achieved for BRCA1 and BRCA2 using PNN are 87.97 % and 82.17 % respectively , while 95.41 % and 92.80 % accuracies achieved using DNN . We propose the Variational InfoMax AutoEncoder ( VIMAE ) , a method to train a generative model , maximizing the variational lower bound of the mutual information between the visible data and the hidden representation , maintaining bounded the capacity of the network . In the paper we investigate the capacity role in a neural network and deduce that a small capacity network tends to learn a more robust and disentangled representation than an high capacity one . Compared with the limited number of labeled images , we can collect a larger number of unlabeled images . To utilize these unlabeled images , we have developed a Convolutional AutoEncoder based Semi-supervised Network ( CAESNet ) for improving the classification performance . MATERIALS AND METHODS We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett 's esophagus at Emory Hospital , which consists of 429 labeled images and 2826 unlabeled images . [ reference ] . In addition , we compare our method with CNN trained only on source samples . We compare our method with Variational Fair AutoEncoder ( VFAE ) [ reference ] and DANN [ reference ] in the Amazon Reviews experiment . Here we propose a deep neural network ( DNN ) as a more accurate regression predictive model . Moreover , to tackle the insufficient data problem , a novel data augmentation algorithm , Protein Solubility Generative Adversarial Nets ( ProGAN ) , was proposed for improving the prediction of protein solubility . After adding mimic data produced from ProGAN , the prediction performance measured by R2 was improved compared with that without data augmentation . A R2 value of 0.4504 was achieved , which was enhanced about 10 % compared with the previous study using the same dataset . Instead of collecting a large amount of MRI data , an approach to image generation has been implemented in our paper . We exploit a progressive growing GAN ( PGGAN ) , a neonatal brain image generation method that can be used for brain MRI classification and ADHD prediction tasks . The PGGAN slowly discovers the features of ADHD in MRI images by adding new layers during the training phase . However , when handling large images , the quality of generated images are not in good quality . Progressive Growing GAN has proved that progressively growing of GANs could generate high pixels images . However , if we simply combine PG-method and CycleGAN , it must bring model collapse . Moreover , I propose Wasserstein GAN-CLS , a new model for conditional image generation based on the Wasserstein distance which offers guarantees of stability . Then , I show how the novel loss function of Wasserstein GAN-CLS can be used in a Conditional Progressive Growing GAN . In combination with the proposed loss , the model boosts by 7.07 % the best Inception Score ( on the Caltech birds dataset ) of the models which use only the sentence-level visual semantics . To capture both the spatial and temporal components of a video sequence , spatio-temporal 3d convolutions are used in all encoder and decoder modules . Further , we utilize concepts of the existing progressively growing GAN ( PGGAN ) that achieves high-quality results on generating high-resolution single images . The FutureGAN model extends this concept to the complex task of video prediction .
To address this problem it is important to know how users interact with the system . This work constitutes a methodological contribution capable of identifying the context of use in which users perform interactions with a groupware application ( synchronous or asynchronous ) and provides , using machine learning techniques , generative models of how users behave . Additionally , these models are transformed into a text that describes in natural language the main characteristics of the interaction of the users with the system .
The multimedia event is directly related to a series of works by Boetti featuring a grid-filling algorithm which has been implemented by the artist on a huge variety of planar supports through many collective projects . In this sense , the event is intended to be another possible realization of the original work exploiting a multimodal support , i.e. including sound ( music and audio processing ) and emphasizing the generative process defined by Boetti 's algorithm through interactivity . An analysis of a corpus of 50 realized Boetti 's works - all based on the same algorithm - has been conducted in order to define meaningful mapping strategies from the visual domain to music and audio-video processing .
When the MAR assumption is incorrect , inferences are biased and predictive performance can suffer . Therefore , we model both the generative process for the data and the missing data mechanism . By learning these two models jointly we obtain improved performance over state-of-the-art methods when predicting the ratings and when modeling the data observation process .
Here , we instead view adversarial attacks as a generative modelling problem , with the goal of producing entire distributions of adversarial examples given an unperturbed input . We show that this generative perspective can be used to design a unified encoder-decoder framework , which is domain-agnostic in that the same framework can be employed to attack different domains with minimal modification . Across three diverse domains --- images , text , and graphs --- our approach generates whitebox attacks with success rates that are competitive with or superior to existing approaches , with a new state-of-the-art achieved in the graph domain .
This may be because the more common use of the subjunctive in English is captured by non-subjunctive structures like infinitival or null CP [ + indicative ] structures . From a generative perspective , the present study investigates second language ( L2 ) syntactic and semantic development of the Spanish subjunctive at the University level . The results of the study reveal that learners at all levels of instruction continued to transfer first language ( L1 ) syntactic rules of English irrealis to L2 structures during acquisition .
During the last decades , a plethora of different and well-adapted models has been developed , but only little attention has been payed to the development of different and similarly well-adapted learning principles . Only recently it was noticed that discriminative learning principles can be superior over generative ones in diverse bioinformatics applications , too . ResultsHere , we propose a generalization of generative and discriminative learning principles containing the maximum likelihood , maximum a posteriori , maximum conditional likelihood , maximum supervised posterior , generative-discriminative trade-off , and penalized generative-discriminative trade-off learning principles as special cases , and we illustrate its efficacy for the recognition of vertebrate transcription factor binding sites .
: subordinate clause , SUBJ : subjunctive , Notations : - : affix boundary , = : clitic boundary 1 . Theoretical approaches to cleft construction Thus far , different approaches have been adopted in order to reveal the true nature of cleft construction among which generative ones enjoy more popularity . These studies fall broadly into two categories : extrapositional and expletive approaches .
In recent years , deep discriminative models have achieved extraordinary performance on supervised learning tasks , significantly outperforming their generative counterparts . However , their success relies on the presence of a large amount of labeled data .
Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot learning setting to upper-bound generalisation errors on unseen tasks and samples . We also propose a generative-based approach to model the shared prior and the posterior of task-specific model parameters more expressively compared to the usual diagonal Gaussian assumption . We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate , with state-of-the-art calibration and classification results on few-shot classification ( mini-ImageNet and tiered-ImageNet ) and regression ( multi-modal task-distribution regression ) benchmarks .
In this paper , we propose a novel technique to recognize faces across age progressions . We also analyze different best in class systems accessible in discriminative-based methodology and generative-based methodology ; this analyzes are performed on different standard age varying face databases .
AbstractMetrics specifying distances between data points can be learned in a discriminative manner or fromgenerative models . In this paper , we show how to unify generative and discriminative learning of met-rics via a kernel learning framework .
It is important to clarify the concept of system . Although the systems theory has shown the generality of the concept of system , it has not gone deep enough to discover the generative nature of systems .
One of the most successful models is the generative adversarial network ( GAN ) gan , which employs a two player min - max game . The generative model , , samples the prior and generates the sample . The discriminator , , is trained to identify whether a point is a sample from the data distribution or a sample from the generative model .
This non - linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research . We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation . Despite widespread use in language modeling and economics , the multinomial likelihood receives less attention in the recommender systems literature .
Factoring out undesired variations from the data can be easily formulated as a general probabilistic model which admits two distinct ( independent ) “ sources ” ; an observed variable , which denotes the variations that we want to remove , and a continuous latent variable which models all the remaining information . This generative process can be formally defined as : where is an appropriate probability distribution for the data we are modelling . With this formulation we explicitly encode a notion of ‘ invariance ’ in our model , since the latent representation is marginally independent of the factors of variation .
Recently , by combining with policy gradient , Generative Adversarial Nets ( GAN ) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation . However , the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process . As such , it limits its success when the length of the generated text samples is long ( more than 20 words ) .
We see that these results are generally in line with the RG and CS metrics , with the Conditional Copy model having the highest number of supporting facts , and the reconstruction terms significantly improving the Joint Copy models . Using a Tukey HSD post - hoc analysis of an ANOVA with the number of contradicting facts as the dependent variable and the generating model and rater i d as independent variables , we found significant ( p < 0.01 ) pairwise differences in contradictory facts between the gold generations and all models except " Copy + Rec + TVD , " as well as a significant difference between " Copy + Rec + TVD " and " Copy " . We similarly found a significant pairwise difference between " Copy + Rec + TVD " and " Copy " for number of supporting facts .
We propose a framework that combines an encoder and a generator to learn disentangled representations which encode meaningful information about the data distribution without the need for any labels . While current approaches focus mostly on the generative aspects of GANs , our framework can be used to perform inference on both real and generated data points . Experiments on several data sets show that the encoder learns interpretable , disentangled representations which encode descriptive properties and can be used to sample images that exhibit specific characteristics . 0
We study the problem of transferring a sample in one domain to an analog sample in another domain . Given two related domains , and , we would like to learn a generative function that maps an input sample from to the domain , such that the output of a given function , which accepts inputs in either domains , would remain unchanged . Other than the function , the training data is unsupervised and consist of a set of samples from each domain .
section : Experiments We present results of our two models both on parsing ( discriminative and generative ) and as a language model ( generative only ) in English and Chinese . paragraph : Data .
Firstly , the salient regions of a down image were cut from the image using the visual saliency model . Then , these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters , which accord with the statistical characteristics of dataset . At last , a DCNN with Inception module and its variants was constructed .
The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals . The results indicate that the autoencoder with nonnegativity constraint ( ANC ) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder . The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel .
This paper tests the hypothesis that information contained in ensemble activity recorded from the primate motor cortex about limb movements is preserved when the activity is projected onto a sparse basis . Spiking rate data from neurons in the motor cortex of an awake behaving macaque monkey was compressed using a sparse autoencoder network , and classifications of movement directions were made in the compressed space . Classifier performance is shown to be similar when using either compressed ( sparsened ) or uncompressed neural activity , demonstrating the potential use of the sparse autoencoder as an unsupervised compression algorithm for low power/low bandwidth wireless transmission of neural ensemble data .
This paper studies the interturn short circuit fault identification method of permanent magnet synchronous motor , and proposes an efficient and accurate interturn short circuit fault identification method based on Generative Adversarial Networks and Sparse Autoencoder Network deep learning . In this method , negative sequence current and torque characteristic signals of permanent magnet synchronous motor are collected , sample data are expanded by means of Generative Adversarial Networks and network , a variety of training sets are constructed , and efficient and accurate fault feature classification and recognition are realized by combining with Sparse Autoencoder Network . The test results show that the accuracy of this method is up to ninety-nine percent .
ABSTRACT In this article , we propose a novel approach based on convolutional features and sparse autoencoder ( AE ) for scene-level land-use ( LU ) classification . This approach starts by generating an initial feature representation of the scenes under analysis from a deep convolutional neural network ( CNN ) pre-learned on a large amount of labelled data from an auxiliary domain .
In this paper , a novel deep learning framework for underwater target classification is proposed . First , instead of extracting features relying on expert knowledge , sparse autoencoder ( AE ) is utilized to learn invariant features from the spectral data of underwater targets . Second , stacked autoencoder ( SAE ) is used to get high-level features as a deep learning method .
The IGMRF prior captures the smoothness as well as preserves sharp discontinuities and the sparsity prior captures the sparseness in the disparity map . We present a sparse autoencoder based approach for learning and inferring the sparse representation of disparities . An iterative two phase algorithm is proposed to solve our energy minimization problem .
In order to perform the regularization , we propose to use the inhomogeneous Gaussian Markov random field ( IGMRF ) and sparsity priors in our energy function . A sparse autoencoder-based approach is proposed for learning and inferring the sparse representation of disparities . The IGMRF prior captures the smoothness as well as preserves sharp discontinuities while the sparsity prior captures the sparseness in the disparity map .
This paper proposes a comprehensive computer-aided diagnosis ( CAD ) scheme for identifying the presence of malaria parasites in thick blood smear images . The parameters of the scheme are pre-trained by functional link artificial neural network followed by sparse stacked autoencoder . The optimum size of the CAD scheme used in this research is 12500 - 2500 - 100 - 50 - 2 , where the input layer has 12500 nodes and Softmax classifier output layer has 2 nodes .
It can serve as a preprocessing step in the pipeline of automated understanding of the semantics of human speech . The paper proposes a Sparse Autoencoder based architecture for Punjabi and English spoken language Classification . A number of shallow architectures namely Soft-max classifier , SVM and deep architectures namely Artificial Neural Networks , SVM with Sparse Auto encoder and Softmax with sparse auto encoder ( with and without fine tuning ) have been compared on the same task .
Fourier transform , principle component analysis , and t-distributed stochastic neighbor embedding methods are applied to visualize the data set , which proves the feasibility of classifying the PD current waveform . Two basic parts of this DL framework are sparse autoencoder layer and softmax layer , the former extracting features of the input signal and the latter operating as the classifier . Hyper-parameters of the network such as sparsity , activation function , number of hidden nodes , and network depth were discussed .
An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results . However , this result can be improved by applying a sparse autoencoder learning algorithm to obtain a more distinguishable diffused image .
Variants of autoencoders as building blocks of pre-trained convolutional layers of neural networks are implemented . A sparse deep autoencoder which minimizes local information entropy on the encoding layer is then proposed and applied to images of size $ $ 2048\times 2048$$2048 × 2048 . We applied this model for feature extraction from pathological images of lung adenocarcinoma , which is comprised of three transcriptome subtypes previously defined by the Cancer Genome Atlas network .
The present paper aims to propose a new neural network called “ sparse semi-autoencoder ” to overcome the vanishing information problem inherent to multi-layered neural networks . The vanishing information problem represents a natural tendency of multi-layered neural networks to lose information in input patterns as well as training errors , including also natural reduction in information due to constraints such as sparse regularization .
Good initialization is essential for training deep models . Hinton and Salakhutdinov adopted the layer - by - layer pretraining strategy to train a deep autoencoder . Krizhevsky initialized the weight of each layer by drawing samples from a Gaussian distribution with zero mean and 0.01 standard deviation .
Among these methods , does this in a “ deep ” way by the layerwise training of a sequence of deep autoencoders , while gradually replacing source - domain samples with target - domain samples . This improves over a similar approach of that simply trains a single deep autoencoder for both domains . In both approaches , the actual classifier / predictor is learned in a separate step using the feature representation learned by autoencoder ( s ) . In contrast to , our approach performs feature learning , domain adaptation and classifier learning jointly , in a unified architecture , and using a single learning algorithm ( backpropagation ) .
In this paper , we proposed to use the GAN framework as a variational inference algorithm for both discrete and continuous latent variables in probabilistic autoencoders . Our method called the adversarial autoencoder ( AAE ) , is a generative autoencoder that achieves competitive test likelihoods on real - valued MNIST and Toronto Face datasets . We discussed how this method can be extended to semi - supervised scenarios and showed that it achieves competitive semi - supervised classification performance on MNIST and SVHN datasets .
document : PixelGAN Autoencoders In this paper , we describe the ‘ ‘ PixelGAN autoencoder ’ ’ , a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels ( PixelCNN ) that is conditioned on a latent code , and the recognition path uses a generative adversarial network ( GAN ) to impose a prior distribution on the latent code . We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder .
Our novel 3D orientation estimation is based on a variant of the Denoising Autoencoder that is trained on simulated views of a 3D model using Domain Randomization . This so - called Augmented Autoencoder has several advantages over existing methods : It does not require real , pose - annotated training data , generalizes to various test sensors and inherently handles object and view symmetries . Instead of learning an explicit mapping from input images to object poses , it provides an implicit representation of object orientations defined by samples in a latent space .
Therefore , models capable of exploiting topological structures of graphs have been shown to achieve superior predictive performances on many LPNC tasks . We present a novel densely connected autoencoder architecture capable of learning a shared representation of latent node embeddings from both local graph topology and available explicit node features for LPNC . The resulting autoencoder models are useful for many applications across multiple domains , including analysis of metabolic networks for drug - target interaction , bibliographic networks , social networks such as Facebook (
For the reconstruction task , our LoNGAE model achieves superior precision@ performance when compared to the related SDNE model . Although both models leverage a deep autoencoder architecture for graph representation learning , the SDNE model lacks several key implementations necessary for enhanced representation capacity , namely parameter sharing between the encoder - decoder parts and end - to - end training of deep architectures . For link prediction , we observe that combining available node features always produces a significant boost in predictive performance .
Zou et al . [ 10 ] learn invariant features from video by enforcing a temporal slowness constraint on the feature representation learned by a linear autoencoder . Sohn and Lee [ 11 ] and Hui
We now describe our work from the perspective of learning autoencoders . Maximum - likelihood estimation in a regular autoencoder takes the following form : There are two key distinctions of note : ( 1 ) The autoencoder ( and denoising autoencoder ) effectively optimizes the first term in the vae objective ( Eq . 5 and Eq . 6 ) using a delta variational distribution
section : Conclusion We introduce the Variational Fair Autoencoder ( VFAE ) , an extension of the semi - supervised variational autoencoder in order to learn representations that are explicitly invariant with respect to some known aspect of a dataset while retaining as much remaining information as possible . We further use a Maximum Mean Discrepancy regularizer in order to further promote invariance in the posterior distribution over latent variables .
Technically , we present a novel adaptive variational AutoEncoder with a convolutional neural network . In the training , this AutoEncoder analyzes publicly available HRTFs dataset and identifies factors that depend on the individuality of users in a nonlinear space . In calibration , the AutoEncoder generates high-quality HRTFs fitted to a specific user by blending the factors .
This problem has received increasing attention from researchers and practitioners recently . Among many approaches developed for detecting and preventing the abnormal accesses to information systems , Shrink AutoEncoder ( SAE ) is an appealing technique due to its simplicity in implementation and effectiveness in detecting network attacks . However , this model has a potential drawback when applying to datasets with the presence of several clusters . The reason is that it attempts to compress all normal data samples into a single cluster in the hidden space of an AutoEncoder . In our research , we introduce a hybrid model between K-means clustering algorithm and SAE to lessen the limitation of SAE in handling such datasets .
This paper proposes a novel Learning Classifier System ( LCS ) which integrates Deep AutoEncoder named DAE to solve high-dimensional problems . In the proposed LCS , DAE starts to compress ( encode ) an environmental input as a high-dimensional information to an input of LCS as a low-dimensional information and decompresses ( decodes ) an output of LCS as a low-dimensional information to a system output as a high-dimensional information .
For unimodal enhancement task , we indicate that the best recognition accuracy of 82.11 % on SEED dataset is achieved with shared representations generated by Deep AutoEncoder ( DAE ) model . For multimodal facilitation tasks , we demonstrate that the Bimodal Deep AutoEncoder ( BDAE ) achieves the mean accuracies of 91.01 % and 83.25 % on SEED and DEAP datasets , respectively , which are much superior to the state-of-the-art approaches . For cross-modal learning task , our experimental results demonstrate that the mean accuracy of 66.34 % is achieved on SEED dataset through shared representations generated by EEG-based DAE as training samples and shared representations generated by eye-based DAE as testing sample , and vice versa .
Feature selection which selects a subset from observed features is a widely used approach for improving performance and effectiveness of machine learning models with high-dimensional data . In this paper , we propose a novel AutoEncoder Feature Selector ( AEFS ) for unsupervised feature selection which combines autoencoder regression and group lasso tasks . Compared to traditional feature selection methods , AEFS can select the most important features by excavating both linear and nonlinear information among features , which is more flexible than the conventional self-representation method for unsupervised feature selection with only linear assumptions .
Feature selection which select a subset of features from original ones has been proven to be effective and efficient to deal with high-dimensional data . In this paper , we propose a novel AutoEncoder Feature Selector ( AEFS ) for unsupervised feature selection . AEFS is based on the autoencoder and the group lasso regularization .
Most existing methods use hand-crafted features in local spatial regions to identify anomalies . In this paper , we propose a novel model called Spatio-Temporal AutoEncoder ( ST AutoEncoder or STAE ) , which utilizes deep neural networks to learn video representation automatically and extracts features from both spatial and temporal dimensions by performing 3-dimensional convolutions . In addition to the reconstruction loss used in existing typical autoencoders , we introduce a weight-decreasing prediction loss for generating future frames , which enhances the motion feature learning in videos .
To deal with it , in this paper , we propose a Neural Network based CCRF ( NN-CCRF ) model that formulates CCRF into an end-to-end neural network framework , which could reduce the complexity in model training and improve the overall performance . We integrate CCRF with NN by introducing a Long Short-Term Memory ( LSTM ) component to learn the non-linear mapping from inputs to outputs of each region , and a modified Stacked Denoising AutoEncoder ( SDAE ) component for pairwise interactions modeling between regions . Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods .
Based on hydrophobicity or hydrophilicity and their role in proteins ' second structure , they are divided into 6 groups , each assigned with scores . The data are prepared in the acceptable form to the automated prediction mechanisms , Probabilistic Neural Network ( PNN ) and Deep Neural NetworkStacked AutoEncoder ( DNN ) . With Jackknife cross validation we show that the prediction accuracy achieved for BRCA1 and BRCA2 using PNN are 87.97 % and 82.17 % respectively , while 95.41 % and 92.80 % accuracies achieved using DNN .
We propose the Variational InfoMax AutoEncoder ( VIMAE ) , a method to train a generative model , maximizing the variational lower bound of the mutual information between the visible data and the hidden representation , maintaining bounded the capacity of the network . In the paper we investigate the capacity role in a neural network and deduce that a small capacity network tends to learn a more robust and disentangled representation than an high capacity one .
Compared with the limited number of labeled images , we can collect a larger number of unlabeled images . To utilize these unlabeled images , we have developed a Convolutional AutoEncoder based Semi-supervised Network ( CAESNet ) for improving the classification performance . MATERIALS AND METHODS We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett 's esophagus at Emory Hospital , which consists of 429 labeled images and 2826 unlabeled images .
[ reference ] . In addition , we compare our method with CNN trained only on source samples . We compare our method with Variational Fair AutoEncoder ( VFAE ) [ reference ] and DANN [ reference ] in the Amazon Reviews experiment .
Here we propose a deep neural network ( DNN ) as a more accurate regression predictive model . Moreover , to tackle the insufficient data problem , a novel data augmentation algorithm , Protein Solubility Generative Adversarial Nets ( ProGAN ) , was proposed for improving the prediction of protein solubility . After adding mimic data produced from ProGAN , the prediction performance measured by R2 was improved compared with that without data augmentation . A R2 value of 0.4504 was achieved , which was enhanced about 10 % compared with the previous study using the same dataset .
Instead of collecting a large amount of MRI data , an approach to image generation has been implemented in our paper . We exploit a progressive growing GAN ( PGGAN ) , a neonatal brain image generation method that can be used for brain MRI classification and ADHD prediction tasks . The PGGAN slowly discovers the features of ADHD in MRI images by adding new layers during the training phase .
However , when handling large images , the quality of generated images are not in good quality . Progressive Growing GAN has proved that progressively growing of GANs could generate high pixels images . However , if we simply combine PG-method and CycleGAN , it must bring model collapse .
Moreover , I propose Wasserstein GAN-CLS , a new model for conditional image generation based on the Wasserstein distance which offers guarantees of stability . Then , I show how the novel loss function of Wasserstein GAN-CLS can be used in a Conditional Progressive Growing GAN . In combination with the proposed loss , the model boosts by 7.07 % the best Inception Score ( on the Caltech birds dataset ) of the models which use only the sentence-level visual semantics .
To capture both the spatial and temporal components of a video sequence , spatio-temporal 3d convolutions are used in all encoder and decoder modules . Further , we utilize concepts of the existing progressively growing GAN ( PGGAN ) that achieves high-quality results on generating high-resolution single images . The FutureGAN model extends this concept to the complex task of video prediction .
User Interaction Modeling and Profile Extraction in Interactive Systems: A Groupware Application Case Study † Alternating from 1 to x and vice versa Probabilistic Matrix Factorization with Non-random Missing Data Generalizable Adversarial Attacks Using Generative Models Syntactic development of the Spanish subjunctive in second language acquisition : complement selection in nominal clauses Unifying generative and discriminative learning principles Persian cleft constructions: A Role and Reference Grammar analysis Unsupervised Feature Learning with Discriminative Encoder PAC-Bayesian Meta-learning with Implicit Prior A Novel Technique to Recognize Human Faces Across Age Progressions Learning Discriminative Metrics via Generative Models and Kernel Learning The Concept of System and the Paradigm of Complexity PixelGAN Autoencoders Variational Autoencoders for Collaborative Filtering The Variational Fair Autoencoder Long Text Generation via Adversarial Training with Leaked Information Challenges in Data-to-Document Generation Inferencing based on unsupervised learning of disentangled representations Unsupervised Cross-Domain Image Generation Recurrent Neural Network Grammars Down image recognition based on deep convolutional neural network Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints Sparse coding of movement-related neural activity Application of Deep Learning in Interturn Short Circuit Fault Diagnosis of PMSM Using convolutional features and a sparse autoencoder for land-use scene classification Deep learning-based recognition of underwater target A regularization framework for stereo matching using IGMRF prior and sparseness learned from autoencoder A learned sparseness and IGMRF-based regularization framework for dense disparity estimation using unsupervised feature learning Unsupervised Deep Learning CAD Scheme for the Detection of Malaria in Blood Smear Microscopic Images A stacked sparse autoencoder based architecture for Punjabi and English spoken language classification using MFCC features Identification of Partial Discharge Defects Based on Deep Learning Method Deep face liveness detection based on nonlinear diffusion using convolution neural network Classification of lung adenocarcinoma transcriptome subtypes from pathological images using deep convolutional networks Sparse semi-autoencoders to solve the vanishing information problem in multi-layered neural networks Learning Feature Pyramids for Human Pose Estimation Unsupervised Domain Adaptation by Backpropagation Adversarial Autoencoders PixelGAN Autoencoders Implicit 3D Orientation Learning for 6D Object Detection from RGB Images Learning to Make Predictions on Graphs with Autoencoders Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Variational Autoencoders for Collaborative Filtering The Variational Fair Autoencoder Fully perceptual-based 3D spatial sound individualization with an adaptive variational autoencoder A Clustering-based Shrink AutoEncoder for Detecting Anomalies in Intrusion Detection Systems Learning classifier system with deep autoencoder Multimodal Emotion Recognition Using Multimodal Deep Learning Autoencoder Inspired Unsupervised Feature Selection Autoencoder Feature Selector Spatio-Temporal AutoEncoder for Video Anomaly Detection Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction Predicting clinical significance of BRCA1 and BRCA2 single nucleotide substitution variants with unknown clinical significance using probabilistic neural network and deep neural network-stacked autoencoder The variational infomax autoencoder CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images Asymmetric Tri-training for Unsupervised Domain Adaptation ProGAN: Protein solubility generative adversarial nets for data augmentation in DNN framework Synthetic Brain Image Generation for ADHD prediction based on Progressive Growing Generative Adversarial Network Attention Guided Unsupervised Image-to-Image Translation with Progressively Growing Strategy Text to Image Synthesis Using Generative Adversarial Networks FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs
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ACM Multimedia ICML NIPS WWW ICLR AAAI EMNLP ESANN ICLR HLT-NAACL DSP ICIP 2017 IEEE International Conference on Computer Vision (ICCV) ICML ArXiv NIPS ECCV 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) NIPS WWW ICLR KSE CEC ICASSP ACM Multimedia IJCAI ICML ACPR Workshops
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