[PDF] Top 20 A Hybrid Convolutional Variational Autoencoder for Text Generation
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A Hybrid Convolutional Variational Autoencoder for Text Generation
... fully convolutional approaches of Dauphin et ...a text fragment dur- ing processing while the existing fully convolu- tional decoders do ...of text samples it is able to ...sized text ... See full document
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Riemannian Normalizing Flow on Variational Wasserstein Autoencoder for Text Modeling
... Many recent studies are proposed to address this key issue. Yang et al. (2017); Semeniuta et al. (2017) use convolutional neural network as de- coder architecture to limit the expressiveness of decoder model. Xu ... See full document
11
Topic Guided Variational Auto Encoder for Text Generation
... using less conditional information while generating each word) (Yang et al., 2017; Shen et al., 2017a), or bridging the amortization gap (between the log- likelihood and the ELBO) using semi-amortized inference networks ... See full document
12
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
... The variational autoencoder (VAE) imposes a probabilistic distribution (typically Gaus- sian) on the latent space and penalizes the Kullback–Leibler (KL) divergence between the posterior and ...for ... See full document
9
On the Importance of the Kullback Leibler Divergence Term in Variational Autoencoders for Text Generation
... generated text: the decoder trained under smaller KL term tends to generate repetitive but mainly plausible sentences, while for larger KL the generated sentences were diverse but inco- ... See full document
10
Semi-supervised adversarial variational autoencoder
... [9], text classification [10], sentence generation [11], speech synthesis and recognition [12] [13] [14], spatio-temporal solar irradiance forecasting [15] and in geoscience for data assimilation ... See full document
17
Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation
... introduce variational autoencoders (VAEs) (Bow- man et ...from text sequences and a decoder takes advantage of the variable to reconstruct word-level ...of text sequences and text ... See full document
11
Learning to Write Stories with Thematic Consistency and Wording Novelty
... story generation, but also essential aspects for any text generation task out- putting a long ...based text generation, one initial attempt for tackling these issues is to combine RNN ... See full document
8
Modeling Event Background for If Then Commonsense Reasoning Using Context aware Variational Autoencoder
... In dialogue generation, Zhao et al. (2017) adapts VAE with encoder-decoder framework to model the latent semantic distribution of answers, which can increase the diversity of generations. For the task of machine ... See full document
10
A Stable Variational Autoencoder for Text Modelling
... We evaluate our model against several strong baselines which apply VAE for text mod- elling (Bowman et al., 2016; Yang et al., 2017; Xu and Durrett, 2018). We conducted experi- ments based on two public benchmark ... See full document
6
Syntax Infused Variational Autoencoder for Text Generation
... syntax-infused variational au- toencoder (SIVAE), that integrates sentences with their syntactic trees to improve the gram- mar of generated ...VAE-based text generative models, SIVAE contains two separate ... See full document
10
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training
... boosting autoencoder with variational infer- ence (Kingma and Welling, 2014), known as vari- ational autoencoder (VAE), can generate not only consistent but also novel and fluent term sequences ... See full document
11
Deep Learning: Approaches and Challenges
... One of the most challenging problems of CNN is its training time, it takes days even weeks to train a model for a very huge dataset. Instead of training for all the data that are redundant and noisy, Liang et. al. [64] ... See full document
8
Diversity aware Event Prediction based on a Conditional Variational Autoencoder with Reconstruction
... the generation of a next event conditioned on a current event, previous work in event prediction employs sequence-to- sequence (seq2seq) ...conditional variational autoen- coder (CVAE) into seq2seq for its ... See full document
10
Long and Diverse Text Generation with Planning based Hierarchical Variational Model
... VHCR in two aspects: (1) VHCR has no plan- ning mechanism, and the global latent variable is mainly designed to address the KL collapse prob- lem, while our global latent variable captures the diversity of reasonable ... See full document
12
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models
... Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information- theoretic variational ... See full document
42
Better Exploiting Latent Variables in Text Modeling
... and text style transfer (Shen et ...tional autoencoder (VAE) (Kingma and Welling, 2014; Rezende et ...to text data, recurrent neural networks are typically utilized for both the encoder and the de- ... See full document
6
Novelty Detection for Multispectral Images with Application to Planetary Exploration
... In this work, we present a system based on convolutional au- toencoders for detecting novel features in multispectral im- ages. We introduce SAMMIE: Selections based on Autoen- coder Modeling of Multispectral ... See full document
8
Generate Faces Using Ladder Variational Autoencoder with Maximum Mean Discrepancy (MMD)
... It has also been observed that the evidence lower bound (ELBO) used in tradi- tional variational autoencoders suffers from uninformative latent feature prob- lem [4] where these models tend to under-use the latent ... See full document
6
Comparative Study of GAN and VAE
... —First criteria to evaluate performance of two generative model is by comparing mean squared error of both of the models.As VAE is taking pixel values of an image as an input, convert it into lower dimensional space and ... See full document
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