Destination prediction is an essential task in many location-based services ( LBS ) such as providing targeted advertisements and route recommendations . Most existing solutions were generative methods that model the problem as a series of probabilistic events that are then used to compute the destination probability using Bayes ’ rule . In contrast , we propose a discriminative method that chooses the most prominent features found in a public trajectory dataset , clusters the trajectories into groups based on these features , and performs destination prediction queries accordingly . A reduction in activity levels may be traced back to 5.3 Human dynamics and internal states 41 its source and we may learn what motivates an individual to be more active . A key assumption is that humans are similar enough that the model can identify a latent space in which data from similar persons with similar behaviour , or whatever drives the generative process , is mapped to the same location in this space . We use the exponential random graph models to understand the network structure and its generative process for the Japanese bipartite network of banks and firms . One of the well-known and simple models of the exponential random graph is the Bernoulli model which shows that the links in the bank – firm network are not independent from each other . However , expanding the scale of high-quality yet structured medical knowledge usually comes with tedious efforts in data preparation and human annotation . To promote the benefits while minimizing the data requirement in expanding medical knowledge , we introduce a generative perspective to study the relational medical entity pair discovery problem . A generative model named Conditional Relationship Variational Autoencoder is proposed to discover meaningful and novel medical entity pairs by purely learning from the expression diversity in the existing relational medical entity pairs . Based on SPOT-5 images and a training on a large coarse resolution dataset , this study tested the performance of these algorithms on 3 cities regarding their geographical location , urban morphology and acquisition dates . Result reveals that discriminative models are more robust than generative ones . It shows that overall accuracy varies from 75 % for the k-means classifier to 85 % for the neural network . Deep speaker embeddings have been demonstrated to outperform their generative counterparts , i-vectors , in recent speaker verification evaluations . To combine the benefits of high performance and generative interpretation , we investigate the use of deep embedding extractor and i-vector extractor in succession . With the rise of deep learning these models were quickly replaced by end-to-end neural networks . More specifically , Deep Neural Networks is a powerful generative-based model to solve the conversational response generation problems . This paper conducted an in-depth survey of recent literature , examining over 70 publications related to chatbots published in the last 5 years . In their original work , [ reference ] report log likelihood estimates from Gaussian Parzen windows , which they admit , has high variance and is known not to perform well in high dimensions . [ reference ] recommend avoiding Parzen windows and argue that generative models should be evaluated with respect to their intended application . [ reference ] suggest an Inception score , however , it assumes labels exist for the dataset . section : Model The generative process we consider in this paper is similar to the deep latent Gaussian model [ reference ] . For each user u We take to be smoothed sentence - level BLEU . We justify training on from a knowledge distillation perspective with the following generative process : suppose that there is a true target sequence ( which we do not observe ) that is first generated from the underlying data distribution . And further suppose that the target sequence that we observe ( ) is a noisy version of the unobserved true sequence : i.e. ( i ) , ( ii ) , where is , for example , a noise function that independently replaces each element in with a random element in with some small probability . Dr . VAE is a deep generative model based on variational autoencoders . Our experimental results showed Dr . The variational auto-encoder ( VAE ) is a powerful and scalable deep generative model . Under the architecture of VAE , the choice of the approximate posterior distribution is one of the crucial issues , and it has a significant impact on tractability and flexibility of the VAE . Generally , latent variables are assumed to be normally distributed with a diagonal covariance matrix , however , it is not flexible enough to match the true complex posterior distribution . However , they often produce " blurry " images , which is linked to their training objective . Sampling in the most popular implementation , the Gaussian VAE , can be interpreted as simply injecting noise to the input of a deterministic decoder . In practice , this simply enforces a smooth latent space structure . The variational autoencoder ( VAE ) is a generative model with continuous latent variables where a pair of probabilistic encoder ( bottom-up ) and decoder ( top-down ) is jointly learned by stochastic gradient variational Bayes . We first elaborate Gaussian VAE , approximating the local covariance matrix of the decoder as an outer product of the principal direction at a position determined by a sample drawn from Gaussian distribution . We show that this model , referred to as VAE-ROC , better captures the data manifold , compared to the standard Gaussian VAE where independent multivariate Gaussian was used to model the decoder . The fine-tuned latent space provides constant weights that are useful for classification . Here we will present the performance analysis of the VAE algorithm with different latent space sizes in the semi-supervised learning using the CIFAR-10 dataset . We evaluate the performance of the coupled VAE model using the MNIST dataset . Compared with the traditional VAE algorithm , the output images generated by the coupled VAE method are clearer and less blurry . The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function : the latent variable has a smaller deviation and the output values are generated by a more compact latent space . Unexpectedly , we prove that the ELBO objective for the linear VAE does not introduce additional spurious local maxima relative to log marginal likelihood . We show further that training a linear VAE with exact variational inference recovers an identifiable global maximum corresponding to the principal component directions . Empirically , we find that our linear analysis is predictive even for high-capacity , non-linear VAEs and helps explain the relationship between the observation noise , local maxima , and posterior collapse in deep Gaussian VAEs . We apply our method to disentangle the semantics and syntax of a sentence and carry out transfer experiments . Polarized-VAE significantly outperforms the VAE baseline and is competitive with the state-of-the-art approaches , while being more a general framework that is applicable to other attribute disentanglement tasks . When trained effectively , the Variational Autoencoder ( VAE ) can be both a powerful generative model and an effective representation learning framework for natural language . In this paper , we propose the first large-scale language VAE model , Optimus . A universal latent embedding space for sentences is first pre-trained on large text corpus , and then fine-tuned for various language generation and understanding tasks . Inspired by breakthroughs in computer vision , DeVLearn represents measurements ( one-dimensional time series data ) as two-dimensional unthresholded Recurrent Plot ( RP ) images . These RP images preserve the temporal relationships present in the original time series and are used to train a deep Variational Auto-Encoder ( VAE ) . The VAE learns the distribution of latent features in the images . Therefore , in this study , we propose two models to prevent this difficulty : JMVAE-kl and JMVAE-h . Results of our experiments demonstrate that these methods can prevent the difficulty above and that they generate modalities bi-directionally with equal or higher likelihood than conventional VAE methods , which generate in only one direction . Moreover , we confirm that these methods can obtain the joint representation appropriately , so that they can generate various variations of modality by moving over the joint representation or changing the value of another modality . First , we reconsider the relationship between vocoder features extracted using the high quality vocoders adopted in conventional VC systems , and hypothesize that the spectral features are in fact F0 dependent . Such hypothesis implies that during the conversion phase , the latent codes and the converted features in VAE based VC are in fact source F0 dependent . To this end , we propose to utilize the F0 as an additional input of the decoder . Additionally , we use our learned acoustic features to argue against the widespread view that mouse ultrasonic vocalizations form discrete syllable categories . Lastly , we present a novel “ shotgun VAE ” that can quantify moment-by-moment variability in vocalizations . In all , we show that data-derived acoustic features confirm and extend existing approaches while offering distinct advantages in several critical applications . Among them , there are two promising ones that are recently pushing the state - of - the - art with highly plausible generated images . The first one is Variational Autoencoders ( VAE ) Gregor2015 , Kingma2013 , Rezende2014 , Kingma2014 , which impose a prior representation space ( e.g. normal distribution ) in order to regularize and constrain the model to sample from it . However , VAEs main limitation is the pixel - wise reconstruction error used as a loss function , which causes the output images to look blurry . If we instead treat as a nuisance variable we are dealing with “ domain adaptation ” , in other words by removing the domain from our representations we will obtain improved performance . In this paper we introduce a novel model based on deep variational autoencoders ( VAE ) kingma2013auto , rezende2014stochastic . We follow the encoder - decoder structure of CNN , with asymmetrically large encoder to extract deep image features , and the decoder part reconstructs dense segmentation masks . We also add the variational autoencoder ( VAE ) branch to the network to reconstruct the input images jointly with segmentation in order to regularize the shared encoder . At inference time , only the main segmentation encode - decoder part is used . This trade - off has resulted in a large variety of generative models , each having their advantages . Most work focuses on stochastic latent variable models such as VAE ’s that aim to extract meaningful representations , but often come with an intractable inference step that can hinder their performance . One effective approach to tractably model a joint distribution of the pixels in the image is to cast it as a product of conditional distributions ; this approach has been adopted in autoregressive models such as NADE and fully visible neural networks . In our experiments , we discuss how the discriminator feature matching loss and the perceptual loss can be jointly used for further improving the performance . We note that a similar loss is used in VAE - GANs . Our full objective combines both GAN loss and feature matching loss as : where controls the importance of the two terms . It is motivated by the belief that the ability to synthesize , or " create " the observed data entails some form of understanding , and it is hoped that a good generative model will automatically learn a disentangled representation , even though it is easy to construct perfect generative models with arbitrarily bad representations . The most prominent generative models are the variational autoencoder ( VAE ) [ reference ] and the generative adversarial network ( GAN ) To show this , we take the RGB image as the input . During training we handle randomness by using either the Mo2 or the VAE method . At test time when the ground truth is unknown , the random numbers are sampled from the predefined distribution . A new architecture of an artificial neural network that helps to generate longer melodic patterns is introduced alongside with methods for post-generation filtering . The proposed approach called variational autoencoder supported by history is based on a recurrent highway gated network combined with a variational autoencoder . Combination of this architecture with filtering heuristics allows generating pseudo-live acoustically pleasing and melodically diverse music . The proposed method leverages the inherent feature similarity from a large number of unlabeled images to train the segmentation model from a smaller number of labeled images . It first learns the parameters of a generative model from unlabeled images using variational autoencoder . The trained generative model provides the feature embedding of the images which allows the clustering of the related observation in the latent feature space . Most of the recent work on multimodal fusion does not guarantee the fidelity of the multimodal representation with respect to the unimodal representations . In this paper , we propose a variational autoencoder-based approach for modality fusion that minimizes information loss between unimodal and multimodal representations . We empirically show that this method outperforms the state-of-the-art methods by a significant margin on several popular datasets . In this paper , we seek a clinically-relevant latent code for representing the spectrum of macular disease . Towards this end , we construct retina-VAE , a variational autoencoder-based model that accepts a patient profile vector ( pVec ) as input . The pVec components include clinical exam findings and demographic information . The context-dependent densities have many potential applications : for example , we show that they can be directly used in the lexical substitution task . We describe an effective estimation method based on the variational autoencoding framework . We also demonstrate that our embeddings achieve competitive results on standard benchmarks . This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs . Using the Wasserstein variational inference framework , we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques . In many settings , the data we model possesses continuous attributes that we would like to take into account at generation time . We propose in this paper GLSR-VAE , a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space . When augmenting the VAE loss with this regularization , changes in the learned latent space reflects changes of the attributes of the data . Results We present single-cell Generative Adversarial Network ( scGAN ) . Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network , which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects . We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder . We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint . Built upon a variational autoencoder based architecture , consisting of a probabilistic encoder and a probabilistic conditional decoder , our model can generate novel exemplars from seen/unseen classes , given their respective class attributes . These exemplars can subsequently be used to train any off-the-shelf classification model . These user similarities are used to link the domains . As a result , we propose a Variational Autoencoder based network model for cross-domain linking with added contextualization to handle sparse data and for better transfer of cross-domain knowledge . We further extend the model to be more suitable in cold start scenarios and to utilize auxiliary user information for additional gains in recommendation accuracy . However , by performing targeted optimization to learn generative models with different rates , we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable . Through experiments on MNIST and Omniglot with a variety of architectures , we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family . Here we present a novel approach to protein design and phenotypic inference using a generative model for protein sequences . BioSeqVAE , a variational autoencoder variant , can hallucinate syntactically valid protein sequences that are likely to fold and function . BioSeqVAE is trained on the entire known protein sequence space and learns to generate valid examples of protein sequences in an unsupervised manner . To alleviate the problem of obtaining data pairs , unpaired image - to - image translation frameworks have been proposed . UNIT combines variational autoencoders ( VAEs ) with CoGAN , a GAN framework where two generators share weights to learn the joint distribution of images in cross domains . CycleGAN and DiscoGAN preserve key attributes between the input and the translated image by utilizing a cycle consistency loss . A different framework for learning density models is autoregressive neural networks such as NADE nade , MADE made , PixelRNN pixelrnn and PixelCNN pixelcnn . Unlike variational autoencoders , which capture the statistics of the data in hierarchical latent codes , the autoregressive models learn the image densities directly at the pixel level without learning a hierarchical latent representation . In this paper , we present the PixelGAN autoencoder as a generative autoencoder that combines the benefits of latent variable models with autoregressive architectures . This is the method that we used in experiments of : mnist ] Figure [ reference ] a. Input Channel . Another method for conditioning is proposed in the PixelVAE pixelvae and the variational lossy autoencoder ( VLAE ) vlae . In this method , first a tensor of size ( batch , width , height , k ) is constructed using the conditioning tensor similar to the location - dependent bias . Therefore the problem of finding an invariant representation for a data point and variation can be cast as performing inference on this graphical model and obtaining the posterior distribution of , . For our model we will employ a variational autoencoder architecture kingma2013auto , rezende2014stochastic ; namely we will parametrize the generative model ( decoder ) and the variational posterior ( encoder ) as ( deep ) neural networks which accept as inputs and respectively and produce the parameters of each distribution after a series of non - linear transformations . Both the model ( ) and variational ( ) The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description . Kulkarni et al . suggest the Deep Convolution Inverse Graphics Network ( DC - IGN ) , which follows the variational autoencoder architecture to transform the input image into different pose and lighting condition . However , both generative CNN and DC - IGN Recent advances in stochastic variational inference hoffman2013stochastic and amortized inference dayan1995helmholtz , mnih2014neural , kingma2013auto , rezende2014stochastic , allowed efficient approximate inference and learning of deep directed graphical models by maximizing a variational lower bound on the log - likelihood neal1998view . In particular , the variational autoencoder algorithm kingma2013auto , rezende2014stochastic simultaneously learns a generative network , that maps gaussian latent variables to samples , and a matched approximate inference network that maps samples to a semantically meaningful latent representation , by exploiting the reparametrization trick williams1992simple . Its success in leveraging recent advances in backpropagation rumelhart1988learning , lecun2012efficient in deep neural networks resulted in its adoption for several applications ranging from speech synthesis chung2015recurrent to language modeling bowman2015generating . Kingma et al . [ reference ] applied a variational - autoencoder - based generative model to semisupervised learning . This work was followed by several variants [ reference ] . Generative adversarial networks ( GANs ) proposed by [ reference ] are a recently popular high - performance framework that can also be applied to semi - supervised learning [ reference ] , [ reference ] . To expand the Scalable Coherent Interface 's ( SCI ) capabilities so it can be used to efficiently handle sharing in systems of hundreds or even thousands of processors , the SCI working group is developing the Kiloprocessor Extensions to SCI . We describe the proposed GLOW and STEM kiloprocessor extensions to SCI . These two sets of extensions provide SCI with scalable reads and scalable writes to widely-shared data . STEM defines algorithms to build and maintain binary sharing trees . We show that latencies of GLOW reads and writes grow only logarithmically with the number of nodes sharing , in contrast to SCI where latencies grow linearly , therefore validating GLOW as a good solution to efficient wide sharing of data . Previous work showed the same for STEM . The survey consist of 3 168 pointings , requiring about 30 PBytes of storage space . As a member of the German Long Wavelength Consortioum ( GLOW ) the Forschungszentrum Julich ( FSZ ) stores in the Long Term Archive ( LTA ) about 50 % of all LoTSS observations conducted to date . In collaboration with SURFsara in Amsterdam we developed service tools that enable the KSP to process LOFAR data stored in the Julich LTA at the supercomputer JUWELS in an automated and robust fashion . The GLOW extensions to cache coherence pmtocofs -previously proposedprovide such support for widely shared data by defining functionality in the network domain . However in their static form the GLOW extensions relied on the user to identify and expose widely shared data to the hardware . This approach suffers because : i ) it requires modification of the programs , ii ) it is not always possible to statically idenhfi the widely shared data , and iii ) it is incompatible with cornmod@ hardware . The TAC KBP entity linking shared task challenges the participants to identify real-word entities and to map them to a knowledgebase of reference entities . This paper describes the GLOW system which participated in the competition and was based on our earlier work ( Ratinov et al. , 2011 ) . It translates text tokens into concept embeddings that are subsequently used as context for disambiguation . In order to test our hypothesis we add our context features to GLOW system considered a baseline . Moreover , we present commonly available dataset for the Disambiguation to Wikipedia task . sc GLOW is implemented in special network switches and incorporates characteristics that are not found together in previous proposals : scalable writes and scalable reads , network locality ( by exploiting the abundance of widely-shared data to satisfy requests locally ) , simplicity , transparency to the base protocol , and network topology independence . With simulation , I show that for programs with wide sharing , sc GLOW can be more than twice as fast as sc SCI in large systems . Furthermore , I examine techniques to identify sharing patterns . The displayed adjacency relations can be selected from an ontology using a set of common configurations , allowing for intuitive discovery of information . Our evaluation demonstrates that the GLOW visualization provides better visual clarity , and displays relations and complex ontologies better than the existing Protégé visualization plug-in Jambalaya . GLOW 3.0 ( or GLOW for short ) , is a Graphical LOTOS Browser , that is , a tool that allows one to produce , examine , modify , save and print graphical representations of any specification written in standard , textual LOTOS . Such graphical representations are conforming to the G-LOTOS syntax discussed in Chapter 20 of this book . Programs that make extensive use of widely shared variables are expected to achieve modest speedups for non-bus-based cache coherence protocoLr , pam’cularly as the number ofprocessors sharing the data grows large . Protocols such as the IEEE Scalable Coherent Interjhce ( SCI ) are optimized for data that is not widely shared ; the GLOW protocol extensions are specljically & signed to address this limitation . The GLO w extensions take advantage of physical locality by mapping K-a~ logical sharing trees to the network topology . The nature of the data allows us to do a direct comparison between the image statistics of the generated samples and the real dataset . We both quantitatively and qualitatively compare state-of-the-art GAN architectures such as DCGAN and LAPGAN against a modification of the latter for the task of image generation at a resolution of 256x256px . Our investigation shows that we can approximate the real data distribution with all of the models , but we notice major differences when visually rating sample realism , diversity and artifacts . Historical attempts to scale up GANs using CNNs to model images have been unsuccessful . This motivated the authors of LAPGAN denton2015deep to develop an alternative approach to iteratively upscale low resolution generated images which can be modeled more reliably . We also encountered difficulties attempting to scale GANs using CNN architectures commonly used in the supervised literature . To this end , several works have attempted to factorize a GAN into a series of GANs , decomposing the difficult task into several more tractable sub - tasks . Denton propose a LAPGAN model that factorizes the generative process into multi - resolution GANs , with each GAN generating a higher - resolution residual conditioned on a lower - resolution image . Although both LAPGAN and SGAN consist of a sequence of GANs each working at one scale , LAPGAN focuses on generating multi - resolution images from coarse to fine while our SGAN aims at modeling multi - level representations from abstract to specific . Wang and Gupta propose a - GAN , using one GAN to generate surface normals and another GAN to generate images conditioned on surface normals . We also compared our model with Fully - Connected ( Fully - Conn ) and Convolutional - Deconvolutional ( Conv - Deconv ) Variational Autoencoders which were trained with the least squares cost function . The LAPGAN model of denton_lapgan was trained on a two level Laplacian Pyramid with a GAN as a top layer generator and all stages were conditioned on the same skipthought vector . In Fig . A group of people walk on a beach with surf boards ” . While all of the samples look sharp , the images generated by LAPGAN look more noisy and it is harder to make out definite objects , whereas the images generated by variational models trained with least squares cost function have a watercolor effect on the images . We found that the quality of generated samples was similar among different variants of conditional DRAW models .
Destination prediction is an essential task in many location-based services ( LBS ) such as providing targeted advertisements and route recommendations . Most existing solutions were generative methods that model the problem as a series of probabilistic events that are then used to compute the destination probability using Bayes ’ rule . In contrast , we propose a discriminative method that chooses the most prominent features found in a public trajectory dataset , clusters the trajectories into groups based on these features , and performs destination prediction queries accordingly .
A reduction in activity levels may be traced back to 5.3 Human dynamics and internal states 41 its source and we may learn what motivates an individual to be more active . A key assumption is that humans are similar enough that the model can identify a latent space in which data from similar persons with similar behaviour , or whatever drives the generative process , is mapped to the same location in this space .
We use the exponential random graph models to understand the network structure and its generative process for the Japanese bipartite network of banks and firms . One of the well-known and simple models of the exponential random graph is the Bernoulli model which shows that the links in the bank – firm network are not independent from each other .
However , expanding the scale of high-quality yet structured medical knowledge usually comes with tedious efforts in data preparation and human annotation . To promote the benefits while minimizing the data requirement in expanding medical knowledge , we introduce a generative perspective to study the relational medical entity pair discovery problem . A generative model named Conditional Relationship Variational Autoencoder is proposed to discover meaningful and novel medical entity pairs by purely learning from the expression diversity in the existing relational medical entity pairs .
Based on SPOT-5 images and a training on a large coarse resolution dataset , this study tested the performance of these algorithms on 3 cities regarding their geographical location , urban morphology and acquisition dates . Result reveals that discriminative models are more robust than generative ones . It shows that overall accuracy varies from 75 % for the k-means classifier to 85 % for the neural network .
Deep speaker embeddings have been demonstrated to outperform their generative counterparts , i-vectors , in recent speaker verification evaluations . To combine the benefits of high performance and generative interpretation , we investigate the use of deep embedding extractor and i-vector extractor in succession .
With the rise of deep learning these models were quickly replaced by end-to-end neural networks . More specifically , Deep Neural Networks is a powerful generative-based model to solve the conversational response generation problems . This paper conducted an in-depth survey of recent literature , examining over 70 publications related to chatbots published in the last 5 years .
In their original work , [ reference ] report log likelihood estimates from Gaussian Parzen windows , which they admit , has high variance and is known not to perform well in high dimensions . [ reference ] recommend avoiding Parzen windows and argue that generative models should be evaluated with respect to their intended application . [ reference ] suggest an Inception score , however , it assumes labels exist for the dataset .
section : Model The generative process we consider in this paper is similar to the deep latent Gaussian model [ reference ] . For each user u
We take to be smoothed sentence - level BLEU . We justify training on from a knowledge distillation perspective with the following generative process : suppose that there is a true target sequence ( which we do not observe ) that is first generated from the underlying data distribution . And further suppose that the target sequence that we observe ( ) is a noisy version of the unobserved true sequence : i.e. ( i ) , ( ii ) , where is , for example , a noise function that independently replaces each element in with a random element in with some small probability .
Dr . VAE is a deep generative model based on variational autoencoders . Our experimental results showed Dr .
The variational auto-encoder ( VAE ) is a powerful and scalable deep generative model . Under the architecture of VAE , the choice of the approximate posterior distribution is one of the crucial issues , and it has a significant impact on tractability and flexibility of the VAE . Generally , latent variables are assumed to be normally distributed with a diagonal covariance matrix , however , it is not flexible enough to match the true complex posterior distribution .
However , they often produce " blurry " images , which is linked to their training objective . Sampling in the most popular implementation , the Gaussian VAE , can be interpreted as simply injecting noise to the input of a deterministic decoder . In practice , this simply enforces a smooth latent space structure .
The variational autoencoder ( VAE ) is a generative model with continuous latent variables where a pair of probabilistic encoder ( bottom-up ) and decoder ( top-down ) is jointly learned by stochastic gradient variational Bayes . We first elaborate Gaussian VAE , approximating the local covariance matrix of the decoder as an outer product of the principal direction at a position determined by a sample drawn from Gaussian distribution . We show that this model , referred to as VAE-ROC , better captures the data manifold , compared to the standard Gaussian VAE where independent multivariate Gaussian was used to model the decoder .
The fine-tuned latent space provides constant weights that are useful for classification . Here we will present the performance analysis of the VAE algorithm with different latent space sizes in the semi-supervised learning using the CIFAR-10 dataset .
We evaluate the performance of the coupled VAE model using the MNIST dataset . Compared with the traditional VAE algorithm , the output images generated by the coupled VAE method are clearer and less blurry . The visualization of the input images embedded in 2D latent variable space provides a deeper insight into the structure of new model with coupled loss function : the latent variable has a smaller deviation and the output values are generated by a more compact latent space .
Unexpectedly , we prove that the ELBO objective for the linear VAE does not introduce additional spurious local maxima relative to log marginal likelihood . We show further that training a linear VAE with exact variational inference recovers an identifiable global maximum corresponding to the principal component directions . Empirically , we find that our linear analysis is predictive even for high-capacity , non-linear VAEs and helps explain the relationship between the observation noise , local maxima , and posterior collapse in deep Gaussian VAEs .
We apply our method to disentangle the semantics and syntax of a sentence and carry out transfer experiments . Polarized-VAE significantly outperforms the VAE baseline and is competitive with the state-of-the-art approaches , while being more a general framework that is applicable to other attribute disentanglement tasks .
When trained effectively , the Variational Autoencoder ( VAE ) can be both a powerful generative model and an effective representation learning framework for natural language . In this paper , we propose the first large-scale language VAE model , Optimus . A universal latent embedding space for sentences is first pre-trained on large text corpus , and then fine-tuned for various language generation and understanding tasks .
Inspired by breakthroughs in computer vision , DeVLearn represents measurements ( one-dimensional time series data ) as two-dimensional unthresholded Recurrent Plot ( RP ) images . These RP images preserve the temporal relationships present in the original time series and are used to train a deep Variational Auto-Encoder ( VAE ) . The VAE learns the distribution of latent features in the images .
Therefore , in this study , we propose two models to prevent this difficulty : JMVAE-kl and JMVAE-h . Results of our experiments demonstrate that these methods can prevent the difficulty above and that they generate modalities bi-directionally with equal or higher likelihood than conventional VAE methods , which generate in only one direction . Moreover , we confirm that these methods can obtain the joint representation appropriately , so that they can generate various variations of modality by moving over the joint representation or changing the value of another modality .
First , we reconsider the relationship between vocoder features extracted using the high quality vocoders adopted in conventional VC systems , and hypothesize that the spectral features are in fact F0 dependent . Such hypothesis implies that during the conversion phase , the latent codes and the converted features in VAE based VC are in fact source F0 dependent . To this end , we propose to utilize the F0 as an additional input of the decoder .
Additionally , we use our learned acoustic features to argue against the widespread view that mouse ultrasonic vocalizations form discrete syllable categories . Lastly , we present a novel “ shotgun VAE ” that can quantify moment-by-moment variability in vocalizations . In all , we show that data-derived acoustic features confirm and extend existing approaches while offering distinct advantages in several critical applications .
Among them , there are two promising ones that are recently pushing the state - of - the - art with highly plausible generated images . The first one is Variational Autoencoders ( VAE ) Gregor2015 , Kingma2013 , Rezende2014 , Kingma2014 , which impose a prior representation space ( e.g. normal distribution ) in order to regularize and constrain the model to sample from it . However , VAEs main limitation is the pixel - wise reconstruction error used as a loss function , which causes the output images to look blurry .
If we instead treat as a nuisance variable we are dealing with “ domain adaptation ” , in other words by removing the domain from our representations we will obtain improved performance . In this paper we introduce a novel model based on deep variational autoencoders ( VAE ) kingma2013auto , rezende2014stochastic .
We follow the encoder - decoder structure of CNN , with asymmetrically large encoder to extract deep image features , and the decoder part reconstructs dense segmentation masks . We also add the variational autoencoder ( VAE ) branch to the network to reconstruct the input images jointly with segmentation in order to regularize the shared encoder . At inference time , only the main segmentation encode - decoder part is used .
This trade - off has resulted in a large variety of generative models , each having their advantages . Most work focuses on stochastic latent variable models such as VAE ’s that aim to extract meaningful representations , but often come with an intractable inference step that can hinder their performance . One effective approach to tractably model a joint distribution of the pixels in the image is to cast it as a product of conditional distributions ; this approach has been adopted in autoregressive models such as NADE and fully visible neural networks .
In our experiments , we discuss how the discriminator feature matching loss and the perceptual loss can be jointly used for further improving the performance . We note that a similar loss is used in VAE - GANs . Our full objective combines both GAN loss and feature matching loss as : where controls the importance of the two terms .
It is motivated by the belief that the ability to synthesize , or " create " the observed data entails some form of understanding , and it is hoped that a good generative model will automatically learn a disentangled representation , even though it is easy to construct perfect generative models with arbitrarily bad representations . The most prominent generative models are the variational autoencoder ( VAE ) [ reference ] and the generative adversarial network ( GAN )
To show this , we take the RGB image as the input . During training we handle randomness by using either the Mo2 or the VAE method . At test time when the ground truth is unknown , the random numbers are sampled from the predefined distribution .
A new architecture of an artificial neural network that helps to generate longer melodic patterns is introduced alongside with methods for post-generation filtering . The proposed approach called variational autoencoder supported by history is based on a recurrent highway gated network combined with a variational autoencoder . Combination of this architecture with filtering heuristics allows generating pseudo-live acoustically pleasing and melodically diverse music .
The proposed method leverages the inherent feature similarity from a large number of unlabeled images to train the segmentation model from a smaller number of labeled images . It first learns the parameters of a generative model from unlabeled images using variational autoencoder . The trained generative model provides the feature embedding of the images which allows the clustering of the related observation in the latent feature space .
Most of the recent work on multimodal fusion does not guarantee the fidelity of the multimodal representation with respect to the unimodal representations . In this paper , we propose a variational autoencoder-based approach for modality fusion that minimizes information loss between unimodal and multimodal representations . We empirically show that this method outperforms the state-of-the-art methods by a significant margin on several popular datasets .
In this paper , we seek a clinically-relevant latent code for representing the spectrum of macular disease . Towards this end , we construct retina-VAE , a variational autoencoder-based model that accepts a patient profile vector ( pVec ) as input . The pVec components include clinical exam findings and demographic information .
The context-dependent densities have many potential applications : for example , we show that they can be directly used in the lexical substitution task . We describe an effective estimation method based on the variational autoencoding framework . We also demonstrate that our embeddings achieve competitive results on standard benchmarks .
This technique results in a very stable likelihood-free training method that can be used with implicit distributions and probabilistic programs . Using the Wasserstein variational inference framework , we introduce several new forms of autoencoders and test their robustness and performance against existing variational autoencoding techniques .
In many settings , the data we model possesses continuous attributes that we would like to take into account at generation time . We propose in this paper GLSR-VAE , a Geodesic Latent Space Regularization for the Variational AutoEncoder architecture and its generalizations which allows a fine control on the embedding of the data into the latent space . When augmenting the VAE loss with this regularization , changes in the learned latent space reflects changes of the attributes of the data .
Results We present single-cell Generative Adversarial Network ( scGAN ) . Our main contribution is to introduce an adversarial network to predict batch effects using the embeddings from the variational autoencoder network , which does not only need to maximize the Negative Binomial data likelihood of the raw scRNA-seq counts but also minimize the correlation between the latent embeddings and the batch effects . We demonstrate scGAN on three public scRNA-seq datasets and show that our method confers superior performance over the state-of-the-art methods in forming clusters of known cell types and identifying known psychiatric genes that are associated with major depressive disorder .
We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint . Built upon a variational autoencoder based architecture , consisting of a probabilistic encoder and a probabilistic conditional decoder , our model can generate novel exemplars from seen/unseen classes , given their respective class attributes . These exemplars can subsequently be used to train any off-the-shelf classification model .
These user similarities are used to link the domains . As a result , we propose a Variational Autoencoder based network model for cross-domain linking with added contextualization to handle sparse data and for better transfer of cross-domain knowledge . We further extend the model to be more suitable in cold start scenarios and to utilize auxiliary user information for additional gains in recommendation accuracy .
However , by performing targeted optimization to learn generative models with different rates , we are able to learn many models that can achieve similar generative performance but make vastly different trade-offs in terms of the usage of the latent variable . Through experiments on MNIST and Omniglot with a variety of architectures , we show how our framework sheds light on many recent proposed extensions to the variational autoencoder family .
Here we present a novel approach to protein design and phenotypic inference using a generative model for protein sequences . BioSeqVAE , a variational autoencoder variant , can hallucinate syntactically valid protein sequences that are likely to fold and function . BioSeqVAE is trained on the entire known protein sequence space and learns to generate valid examples of protein sequences in an unsupervised manner .
To alleviate the problem of obtaining data pairs , unpaired image - to - image translation frameworks have been proposed . UNIT combines variational autoencoders ( VAEs ) with CoGAN , a GAN framework where two generators share weights to learn the joint distribution of images in cross domains . CycleGAN and DiscoGAN preserve key attributes between the input and the translated image by utilizing a cycle consistency loss .
A different framework for learning density models is autoregressive neural networks such as NADE nade , MADE made , PixelRNN pixelrnn and PixelCNN pixelcnn . Unlike variational autoencoders , which capture the statistics of the data in hierarchical latent codes , the autoregressive models learn the image densities directly at the pixel level without learning a hierarchical latent representation . In this paper , we present the PixelGAN autoencoder as a generative autoencoder that combines the benefits of latent variable models with autoregressive architectures .
This is the method that we used in experiments of : mnist ] Figure [ reference ] a. Input Channel . Another method for conditioning is proposed in the PixelVAE pixelvae and the variational lossy autoencoder ( VLAE ) vlae . In this method , first a tensor of size ( batch , width , height , k ) is constructed using the conditioning tensor similar to the location - dependent bias .
Therefore the problem of finding an invariant representation for a data point and variation can be cast as performing inference on this graphical model and obtaining the posterior distribution of , . For our model we will employ a variational autoencoder architecture kingma2013auto , rezende2014stochastic ; namely we will parametrize the generative model ( decoder ) and the variational posterior ( encoder ) as ( deep ) neural networks which accept as inputs and respectively and produce the parameters of each distribution after a series of non - linear transformations . Both the model ( ) and variational ( )
The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description . Kulkarni et al . suggest the Deep Convolution Inverse Graphics Network ( DC - IGN ) , which follows the variational autoencoder architecture to transform the input image into different pose and lighting condition . However , both generative CNN and DC - IGN
Recent advances in stochastic variational inference hoffman2013stochastic and amortized inference dayan1995helmholtz , mnih2014neural , kingma2013auto , rezende2014stochastic , allowed efficient approximate inference and learning of deep directed graphical models by maximizing a variational lower bound on the log - likelihood neal1998view . In particular , the variational autoencoder algorithm kingma2013auto , rezende2014stochastic simultaneously learns a generative network , that maps gaussian latent variables to samples , and a matched approximate inference network that maps samples to a semantically meaningful latent representation , by exploiting the reparametrization trick williams1992simple . Its success in leveraging recent advances in backpropagation rumelhart1988learning , lecun2012efficient in deep neural networks resulted in its adoption for several applications ranging from speech synthesis chung2015recurrent to language modeling bowman2015generating .
Kingma et al . [ reference ] applied a variational - autoencoder - based generative model to semisupervised learning . This work was followed by several variants [ reference ] . Generative adversarial networks ( GANs ) proposed by [ reference ] are a recently popular high - performance framework that can also be applied to semi - supervised learning [ reference ] , [ reference ] .
To expand the Scalable Coherent Interface 's ( SCI ) capabilities so it can be used to efficiently handle sharing in systems of hundreds or even thousands of processors , the SCI working group is developing the Kiloprocessor Extensions to SCI . We describe the proposed GLOW and STEM kiloprocessor extensions to SCI . These two sets of extensions provide SCI with scalable reads and scalable writes to widely-shared data .
STEM defines algorithms to build and maintain binary sharing trees . We show that latencies of GLOW reads and writes grow only logarithmically with the number of nodes sharing , in contrast to SCI where latencies grow linearly , therefore validating GLOW as a good solution to efficient wide sharing of data . Previous work showed the same for STEM .
The survey consist of 3 168 pointings , requiring about 30 PBytes of storage space . As a member of the German Long Wavelength Consortioum ( GLOW ) the Forschungszentrum Julich ( FSZ ) stores in the Long Term Archive ( LTA ) about 50 % of all LoTSS observations conducted to date . In collaboration with SURFsara in Amsterdam we developed service tools that enable the KSP to process LOFAR data stored in the Julich LTA at the supercomputer JUWELS in an automated and robust fashion .
The GLOW extensions to cache coherence pmtocofs -previously proposedprovide such support for widely shared data by defining functionality in the network domain . However in their static form the GLOW extensions relied on the user to identify and expose widely shared data to the hardware . This approach suffers because : i ) it requires modification of the programs , ii ) it is not always possible to statically idenhfi the widely shared data , and iii ) it is incompatible with cornmod@ hardware .
The TAC KBP entity linking shared task challenges the participants to identify real-word entities and to map them to a knowledgebase of reference entities . This paper describes the GLOW system which participated in the competition and was based on our earlier work ( Ratinov et al. , 2011 ) .
It translates text tokens into concept embeddings that are subsequently used as context for disambiguation . In order to test our hypothesis we add our context features to GLOW system considered a baseline . Moreover , we present commonly available dataset for the Disambiguation to Wikipedia task .
sc GLOW is implemented in special network switches and incorporates characteristics that are not found together in previous proposals : scalable writes and scalable reads , network locality ( by exploiting the abundance of widely-shared data to satisfy requests locally ) , simplicity , transparency to the base protocol , and network topology independence . With simulation , I show that for programs with wide sharing , sc GLOW can be more than twice as fast as sc SCI in large systems . Furthermore , I examine techniques to identify sharing patterns .
The displayed adjacency relations can be selected from an ontology using a set of common configurations , allowing for intuitive discovery of information . Our evaluation demonstrates that the GLOW visualization provides better visual clarity , and displays relations and complex ontologies better than the existing Protégé visualization plug-in Jambalaya .
GLOW 3.0 ( or GLOW for short ) , is a Graphical LOTOS Browser , that is , a tool that allows one to produce , examine , modify , save and print graphical representations of any specification written in standard , textual LOTOS . Such graphical representations are conforming to the G-LOTOS syntax discussed in Chapter 20 of this book .
Programs that make extensive use of widely shared variables are expected to achieve modest speedups for non-bus-based cache coherence protocoLr , pam’cularly as the number ofprocessors sharing the data grows large . Protocols such as the IEEE Scalable Coherent Interjhce ( SCI ) are optimized for data that is not widely shared ; the GLOW protocol extensions are specljically & signed to address this limitation . The GLO w extensions take advantage of physical locality by mapping K-a~ logical sharing trees to the network topology .
The nature of the data allows us to do a direct comparison between the image statistics of the generated samples and the real dataset . We both quantitatively and qualitatively compare state-of-the-art GAN architectures such as DCGAN and LAPGAN against a modification of the latter for the task of image generation at a resolution of 256x256px . Our investigation shows that we can approximate the real data distribution with all of the models , but we notice major differences when visually rating sample realism , diversity and artifacts .
Historical attempts to scale up GANs using CNNs to model images have been unsuccessful . This motivated the authors of LAPGAN denton2015deep to develop an alternative approach to iteratively upscale low resolution generated images which can be modeled more reliably . We also encountered difficulties attempting to scale GANs using CNN architectures commonly used in the supervised literature .
To this end , several works have attempted to factorize a GAN into a series of GANs , decomposing the difficult task into several more tractable sub - tasks . Denton propose a LAPGAN model that factorizes the generative process into multi - resolution GANs , with each GAN generating a higher - resolution residual conditioned on a lower - resolution image . Although both LAPGAN and SGAN consist of a sequence of GANs each working at one scale , LAPGAN focuses on generating multi - resolution images from coarse to fine while our SGAN aims at modeling multi - level representations from abstract to specific . Wang and Gupta propose a - GAN , using one GAN to generate surface normals and another GAN to generate images conditioned on surface normals .
We also compared our model with Fully - Connected ( Fully - Conn ) and Convolutional - Deconvolutional ( Conv - Deconv ) Variational Autoencoders which were trained with the least squares cost function . The LAPGAN model of denton_lapgan was trained on a two level Laplacian Pyramid with a GAN as a top layer generator and all stages were conditioned on the same skipthought vector . In Fig .
A group of people walk on a beach with surf boards ” . While all of the samples look sharp , the images generated by LAPGAN look more noisy and it is harder to make out definite objects , whereas the images generated by variational models trained with least squares cost function have a watercolor effect on the images . We found that the quality of generated samples was similar among different variants of conditional DRAW models .
Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets Monitoring and modelling of behavioural changes using smartphone and wearable sensing Exponential random graph models for the Japanese bipartite network of banks and firms On the Generative Discovery of Structured Medical Knowledge Mapping tree cover in European cities: Comparison of classification algorithms for an operational production framework Neural i-vectors A Survey on Chatbot Implementation in Customer Service Industry through Deep Neural Networks Generative Multi-Adversarial Networks Variational Autoencoders for Collaborative Filtering Sequence-Level Knowledge Distillation Dr.VAE: improving drug response prediction via modeling of drug perturbation effects Variational inference with Gaussian mixture model and householder flow From Variational to Deterministic Autoencoders Gaussian Copula Variational Autoencoders for Mixed Data Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation Optimus: Organizing Sentences via Pre-trained Modeling of a Latent Space DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems Improving Bi-directional Generation between Different Modalities with Variational Autoencoders Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion Inferring low-dimensional latent descriptions of animal vocalizations Invertible Conditional GANs for image editing The Variational Fair Autoencoder 3D MRI brain tumor segmentation using autoencoder regularization Pixel Recurrent Neural Networks High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets A Point Set Generation Network for 3D Object Reconstruction from a Single Image Music generation with variational recurrent autoencoder supported by history Semi-supervised Segmentation of Optic Cup in Retinal Fundus Images Using Variational Autoencoder Variational Fusion for Multimodal Sentiment Analysis retina-VAE: Variationally Decoding the Spectrum of Macular Disease Embedding Words as Distributions with a Bayesian Skip-gram Model Wasserstein Variational Inference GLSR-VAE: Geodesic latent space regularization for variational autoencoder architectures Deep feature extraction of single-cell transcriptomes by generative adversarial network Generalized Zero-Shot Learning via Synthesized Examples Latent User Linking for Collaborative Cross Domain Recommendation An Information-Theoretic Analysis of Deep Latent-Variable Models How to Hallucinate Functional Proteins. StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation PixelGAN Autoencoders The Variational Fair Autoencoder Deep Identity-aware Transfer of Facial Attributes Density estimation using Real NVP Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Kiloprocessor Extensions to SCI Realising the LOFAR Two-Metre Sky Survey -- using the supercomputer JUWELS at the Forschungszentrum Jülich A study of three dynamic approaches to handle widely shared data in shared-memory multiprocessors GLOW TAC-KBP2011 Entity Linking System Smart Context Generation for Disambiguation to Wikipedia Identification and optimization of sharing patterns for scalable shared-memory multiprocessors Using Hierarchical Edge Bundles to visualize complex ontologies in GLOW GLOW 3.0 - A GRAPHICAL LOTOS BROWSER The GLOW cache coherence protocol extensions for widely shared data MelanoGANs: High Resolution Skin Lesion Synthesis with GANs Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Stacked Generative Adversarial Networks Generating Images from Captions with Attention
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KDD JURSE ICEBE ICLR WWW EMNLP ArXiv ICLR BrainLes@MICCAI ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) MICCAI (2) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition NIPS ICLR ArXiv ICLR IEEE Transactions on Pattern Analysis and Machine Intelligence IPPS International Conference on Supercomputing TAC SAC International Conference on Supercomputing ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR
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