Inference is performed via variational inference to approximate the posterior of the model. - Approximate with samples of z This paper proposes a deep generative model for community detection and network generation. Variational autoencoder (VAE) was first proposed in this paper by Kingma and Max Welling. Recently, it has been shown that variational autoencoders (VAEs) can be successfully trained to learn such codes in unsupervised and semi-supervised scenarios. A noise reduction mechanism is designed for variational autoencoder in input layer of text feature extraction to reduce noise interference and improve robustness and feature discrimination of the model. x�Z�r����+���Zf�EJq���SY�^ؽ IHD7 �$+ߙl�[rν�a a9�߄;�;>}r~v>9�%~�l��i Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Variational Autoencoder for Semi-Supervised Text Classiﬁcation Weidi Xu, Haoze Sun, Chao Deng, Ying Tan Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China wead hsu@pku.edu.cn, … arXiv:1907.08956. A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. What is the loss, how define, what is the term, why is that? deep variational inference framework that is specifically designed to infer the causality of spillover effects between pairs of units. A novel variational autoencoder is developed to model images, as well as associated labels or captions. This is my reproduced Graph AutoEncoder （GAE） and variational Graph AutoEncoder (VGAE) by the Pytorch. VAEs are appealing because they are built on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. This paper is a study on Dirichlet prior in variational autoencoder. A Variational Autoencoder is a type of likelihood-based generative model. Reviewer 1 Summary. O�\^yn�e_������0�j` j1�L$�*�(��(�݃nW���n_#/� �G�F��Yx��VjA?���T�%�'�$�ñ� Why use that constant and this prior? In the example above, we've described the input image in terms of its latent attributes using a single value to describe each a… Autoencoder. There are two layers used to calculate the mean and variance for each sample. Chapter 4 Causal effect variational autoencoder. Why use the propose architecture? Browse our catalogue of tasks and access state-of-the-art solutions. AE, AD represent arithmetic encoder and arithmetic de-coder. Cite this paper as: Zhao Q., Adeli E., Honnorat N., Leng T., Pohl K.M. VAEs have been traditionally hard to train at high resolutions and unstable when going deep with many layers. This is the implementation of paper 'Variational Graph Auto-Encoder' in NIPS Workshop on Bayesian Deep Learning, 2016. Variational autoencoders (VAEs) are a deep learning technique for learning latent representations. Our model produces more meaningful and interpretable latent representation with no component collapsing compared to baseline variational autoehcoders. - z ~ P(z), which we can sample from, such as a Gaussian distribution. Variational autoencoders can perform where PCA doesn't. The proposed framework is based on using Deep Generative Deconvolutional Networks (DGDNs) as a decoders of the latent image features, and a deep Convolutional Neural Network (CNN) as the encoder which approximates the … While this is promising, the road to a fully autonomous unsupervised detection of a phase transition that we did not know before seems still to be a long one. It consists of an encoder, that takes in data$x$as input and transforms this into a latent representation$z$, and a decoder, that takes a latent representation$z$and returns a reconstruction$\hat{x}$. Autoencoder is a neural network designed to learn an identity function in an unsupervised way to reconstruct the original input while compressing the data in the process so as to discover a more efficient and compressed representation. To provide an example, let's suppose we've trained an autoencoder model on a large dataset of faces with a encoding dimension of 6. A Linear VAE Perspective on Posterior Collapse, Enhancing Variational Autoencoders with Mutual Information Neural Estimation for Text Generation, Wavelets to the Rescue: Improving Sample Quality of Latent Variable Deep Generative Models, Study of Deep Generative Models for Inorganic Chemical Compositions, Optimal Transport Based Generative Autoencoders, Label-Conditioned Next-Frame Video Generation with Neural Flows, Robust Ordinal VAE: Employing Noisy Pairwise Comparisons for Disentanglement, Man-in-the-Middle Attacks against Machine Learning Classifiers via Malicious Generative Models, A Generative Approach Towards Improved Robotic Detection of Marine Litter, A Joint Model for Anomaly Detection and Trend Prediction on IT Operation Series, Variational autoencoder reconstruction of complex many-body physics, Conditional out-of-sample generation for unpaired data using trVAE, DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps, Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks, Deep Clustering by Gaussian Mixture Variational Autoencoders With Graph Embedding, On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation, MG-VAE: Deep Chinese Folk Songs Generation with Specific Regional Style, Implicit Discriminator in Variational Autoencoder, "Best-of-Many-Samples" Distribution Matching, Disentangling Speech and Non-Speech Components for Building Robust Acoustic Models from Found Data, Learning to Conceal: A Deep Learning Based Method for Preserving Privacy and Avoiding Prejudice, Scalable Deep Unsupervised Clustering with Concrete GMVAEs, Prediction of rare feature combinations in population synthesis: Application of deep generative modelling, Many-to-Many Voice Conversion using Cycle-Consistent Variational Autoencoder with Multiple Decoders,$ρ$-VAE: Autoregressive parametrization of the VAE encoder, Generating Data using Monte Carlo Dropout, Balancing Reconstruction Quality and Regularisation in ELBO for VAEs, Neural Gaussian Copula for Variational Autoencoder, MIDI-Sandwich2: RNN-based Hierarchical Multi-modal Fusion Generation VAE networks for multi-track symbolic music generation, Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement, Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations, Improving Disentangled Representation Learning with the Beta Bernoulli Process, Document Hashing with Mixture-Prior Generative Models, PaccMann$^{RL}\$: Designing anticancer drugs from transcriptomic data via reinforcement learning, PixelVAE++: Improved PixelVAE with Discrete Prior, Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs, Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma, Improve variational autoEncoder with auxiliary softmax multiclassifier, Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders, Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model, SDM-NET: Deep Generative Network for Structured Deformable Mesh, Augmenting Variational Autoencoders with Sparse Labels: A Unified Framework for Unsupervised, Semi-(un)supervised, and Supervised Learning, Audio-visual Speech Enhancement Using Conditional Variational Auto-Encoders, Mesh Variational Autoencoders with Edge Contraction Pooling, Learning to Dress 3D People in Generative Clothing, GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations, Noise Contrastive Variational Autoencoders, The continuous Bernoulli: fixing a pervasive error in variational autoencoders, retina-VAE: Variationally Decoding the Spectrum of Macular Disease, Out-of-Distribution Detection Using Neural Rendering Generative Models, GP-VAE: Deep Probabilistic Time Series Imputation, VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly Detection, Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization, Disentangled Inference for GANs with Latently Invertible Autoencoder, Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation, Modality Conversion of Handwritten Patterns by Cross Variational Autoencoders, A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation, Generating and Exploiting Probabilistic Monocular Depth Estimates, MONOCULAR DEPTH ESTIMATION ON NYU-DEPTH V2, Using generative modelling to produce varied intonation for speech synthesis, Strategies to architect AI Safety: Defense to guard AI from Adversaries, Learning to regularize with a variational autoencoder for hydrologic inverse analysis, Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training, Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder, Improving VAEs' Robustness to Adversarial Attack, On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder, Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning, Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning, OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization, Gravity-Inspired Graph Autoencoders for Directed Link Prediction, An Interactive Insight Identification and Annotation Framework for Power Grid Pixel Maps using DenseU-Hierarchical VAE, Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders, Joint haze image synthesis and dehazing with mmd-vae losses, Generative Modeling and Inverse Imaging of Cardiac Transmembrane Potential, Adversarial Variational Embedding for Robust Semi-supervised Learning, A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders, Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion, Towards a better understanding of Vector Quantized Autoencoders, Learning Latent Semantic Representation from Pre-defined Generative Model, Deep Generative Models for learning Coherent Latent Representations from Multi-Modal Data, ISA-VAE: Independent Subspace Analysis with Variational Autoencoders, Generated Loss and Augmented Training of MNIST VAE, Generated Loss, Augmented Training, and Multiscale VAE, TransGaGa: Geometry-Aware Unsupervised Image-to-Image Translation, Distributed generation of privacy preserving data with user customization, Variational AutoEncoder For Regression: Application to Brain Aging Analysis, A Variational Auto-Encoder Model for Stochastic Point Processes, From Variational to Deterministic Autoencoders, An Alarm System For Segmentation Algorithm Based On Shape Model, Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing, f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learning, Generative Models For Deep Learning with Very Scarce Data, A Degeneracy Framework for Scalable Graph Autoencoders, Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins, WiSE-ALE: Wide Sample Estimator for Approximate Latent Embedding, Contrastive Variational Autoencoder Enhances Salient Features, Truncated Gaussian-Mixture Variational AutoEncoder, BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling, GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping, Relevance Factor VAE: Learning and Identifying Disentangled Factors, Adversarial Networks and Autoencoders: The Primal-Dual Relationship and Generalization Bounds, A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids, Towards Generating Long and Coherent Text with Multi-Level Latent Variable Models, Uncertainty Quantification in Deep MRI Reconstruction, Unsupervised speech representation learning using WaveNet autoencoders, Deep Generative Learning via Variational Gradient Flow, MONet: Unsupervised Scene Decomposition and Representation, Lagging Inference Networks and Posterior Collapse in Variational Autoencoders, Practical Lossless Compression with Latent Variables using Bits Back Coding, Tree Tensor Networks for Generative Modeling, MAE: Mutual Posterior-Divergence Regularization for Variational AutoEncoders, Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions, Variational Autoencoders Pursue PCA Directions (by Accident), Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier, Learning Latent Subspaces in Variational Autoencoders, A Probe Towards Understanding GAN and VAE Models, Learning latent representations for style control and transfer in end-to-end speech synthesis, Adversarial Defense of Image Classification Using a Variational Auto-Encoder, Disentangling Disentanglement in Variational Autoencoders, Embedding-reparameterization procedure for manifold-valued latent variables in generative models, Variational Autoencoding the Lagrangian Trajectories of Particles in a Combustion System, Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion, Sequential Variational Autoencoders for Collaborative Filtering, An Interpretable Generative Model for Handwritten Digit Image Synthesis, Disentangling Latent Factors of Variational Auto-Encoder with Whitening, Simple, Distributed, and Accelerated Probabilistic Programming, Audio Source Separation Using Variational Autoencoders and Weak Class Supervision, Resampled Priors for Variational Autoencoders, PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences, Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation, Encoding Robust Representation for Graph Generation, LINK PREDICTION ON CORA (BIASED EVALUATION), Open-Ended Content-Style Recombination Via Leakage Filtering, A Deep Generative Model for Semi-Supervised Classification with Noisy Labels, Variational Autoencoder with Implicit Optimal Priors, Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder, Hyperprior Induced Unsupervised Disentanglement of Latent Representations, Coordinated Heterogeneous Distributed Perception based on Latent Space Representation, Classification by Re-generation: Towards Classification Based on Variational Inference, Molecular Hypergraph Grammar with its Application to Molecular Optimization, Discovering Influential Factors in Variational Autoencoder, Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders, Scalable Population Synthesis with Deep Generative Modeling, Synthetic Patient Generation: A Deep Learning Approach Using Variational Autoencoders, ACVAE-VC: Non-parallel many-to-many voice conversion with auxiliary classifier variational autoencoder, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects, Learning disentangled representation from 12-lead electrograms: application in localizing the origin of Ventricular Tachycardia, Bounded Information Rate Variational Autoencoders, Item Recommendation with Variational Autoencoders and Heterogenous Priors, Variational Inference: A Unified Framework of Generative Models and Some Revelations, A Hybrid Variational Autoencoder for Collaborative Filtering, Explorations in Homeomorphic Variational Auto-Encoding, Avoiding Latent Variable Collapse With Generative Skip Models, An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution, A Variational Time Series Feature Extractor for Action Prediction, Learning a Representation Map for Robot Navigation using Deep Variational Autoencoder, New Losses for Generative Adversarial Learning, Anomaly Detection for Skin Disease Images Using Variational Autoencoder, Expanding variational autoencoders for learning and exploiting latent representations in search distributions, oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis, Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation, Improving latent variable descriptiveness with AutoGen, q-Space Novelty Detection with Variational Autoencoders, Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder, Deep learning based inverse method for layout design, Fast, Diverse and Accurate Image Captioning Guided By Part-of-Speech, DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder, Theory and Experiments on Vector Quantized Autoencoders, Conditional Inference in Pre-trained Variational Autoencoders via Cross-coding, Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation, Mask-aware Photorealistic Face Attribute Manipulation, Functional Generative Design: An Evolutionary Approach to 3D-Printing, Group Anomaly Detection using Deep Generative Models, Binge Watching: Scaling Affordance Learning from Sitcoms, Expressive Speech Synthesis via Modeling Expressions with Variational Autoencoder, Variational Message Passing with Structured Inference Networks, A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music, Learning from Noisy Web Data with Category-level Supervision, Blind Channel Equalization using Variational Autoencoders, Degeneration in VAE: in the Light of Fisher Information Loss, Interpretable VAEs for nonlinear group factor analysis, Auto-Encoding Total Correlation Explanation, TVAE: Triplet-Based Variational Autoencoder using Metric Learning, Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications, Preliminary theoretical troubleshooting in Variational Autoencoder, The Mutual Autoencoder: Controlling Information in Latent Code Representations, Interpretable Classification via Supervised Variational Autoencoders and Differentiable Decision Trees, Evaluation of generative networks through their data augmentation capacity, The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling, Nonparametric Inference for Auto-Encoding Variational Bayes, Concept Formation and Dynamics of Repeated Inference in Deep Generative Models, Spatial PixelCNN: Generating Images from Patches, Text Generation Based on Generative Adversarial Nets with Latent Variable, MR image reconstruction using deep density priors, Hybrid VAE: Improving Deep Generative Models using Partial Observations, A Classifying Variational Autoencoder with Application to Polyphonic Music Generation, Zero-Shot Learning via Class-Conditioned Deep Generative Models, Learnable Explicit Density for Continuous Latent Space and Variational Inference, Disentangled Variational Auto-Encoder for Semi-supervised Learning, A Deep Generative Framework for Paraphrase Generation, Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories, Symmetric Variational Autoencoder and Connections to Adversarial Learning, Sequence to Better Sequence: Continuous Revision of Combinatorial Structures, GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures, Hidden Talents of the Variational Autoencoder, Tackling Over-pruning in Variational Autoencoders, Generative Models of Visually Grounded Imagination, Investigation of Using VAE for i-Vector Speaker Verification, Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image Generation, The Pose Knows: Video Forecasting by Generating Pose Futures, beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Learning Latent Representations for Speech Generation and Transformation, DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding, Towards Deeper Understanding of Variational Autoencoding Models, Improved Variational Autoencoders for Text Modeling using Dilated Convolutions, Adversarial examples for generative models, A Hybrid Convolutional Variational Autoencoder for Text Generation, Authoring image decompositions with generative models, Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive Architectures, Semantic Facial Expression Editing using Autoencoded Flow, Improving Variational Auto-Encoders using Householder Flow, Deep Variational Inference Without Pixel-Wise Reconstruction, PixelVAE: A Latent Variable Model for Natural Images, Deep Feature Consistent Variational Autoencoder, Neural Photo Editing with Introspective Adversarial Networks, Gaussian Copula Variational Autoencoders for Mixed Data, Discriminative Regularization for Generative Models, Autoencoding beyond pixels using a learned similarity metric, Cascading Denoising Auto-Encoder as a Deep Directed Generative Model. This paper presents a new variational autoencoder (VAE) for images, which also is capable of predicting labels and captions. methods/Screen_Shot_2020-07-07_at_4.47.56_PM_Y06uCVO.png, Disentangled Recurrent Wasserstein Autoencoder, Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning, NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection, HAVANA: Hierarchical and Variation-Normalized Autoencoder for Person Re-identification, TextBox: A Unified, Modularized, and Extensible Framework for Text Generation, Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey, Direct Evolutionary Optimization of Variational Autoencoders with Binary Latents, Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables, Self-Supervised Variational Auto-Encoders, Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images, Mixture Representation Learning with Coupled Autoencoding Agents, Quantitative Understanding of VAE as a Non-linearly Scaled Isometric Embedding, Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble, Guiding Representation Learning in Deep Generative Models with Policy Gradients, Bigeminal Priors Variational Auto-encoder, Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks, AriEL: Volume Coding for Sentence Generation Comparisons, Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling, Variance Reduction in Hierarchical Variational Autoencoders, Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration, Decoupling Global and Local Representations via Invertible Generative Flows, LATENT OPTIMIZATION VARIATIONAL AUTOENCODER FOR CONDITIONAL MOLECULAR GENERATION, Property Controllable Variational Autoencoder via Invertible Mutual Dependence, AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE, AC-VAE: Learning Semantic Representation with VAE for Adaptive Clustering, Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders, GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations, Unsupervised Discovery of Interpretable Latent Manipulations in Language VAEs, Unsupervised Learning of Slow Features for Data Efficient Regression, On the Importance of Looking at the Manifold, Infer-AVAE: An Attribute Inference Model Based on Adversarial Variational Autoencoder, Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler, Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder, Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations, AVAE: Adversarial Variational Auto Encoder, Populating 3D Scenes by Learning Human-Scene Interaction, Parallel WaveNet conditioned on VAE latent vectors, Automated 3D cephalometric landmark identification using computerized tomography, Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments, Unsupervised Learning of slow features for Data Efficient Regression, Generative Capacity of Probabilistic Protein Sequence Models, Learning Disentangled Latent Factors from Paired Data in Cross-Modal Retrieval: An Implicit Identifiable VAE Approach, Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks, Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation, Predicting S&P500 Index direction with Transfer Learning and a Causal Graph as main Input, Dual Contradistinctive Generative Autoencoder, End-To-End Dilated Variational Autoencoder with Bottleneck Discriminative Loss for Sound Morphing -- A Preliminary Study, Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders, Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data, On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision, VCE: Variational Convertor-Encoder for One-Shot Generalization, PRVNet: Variational Autoencoders for Massive MIMO CSI Feedback, Improving Variational Autoencoder for Text Modelling with Timestep-Wise Regularisation, ControlVAE: Tuning, Analytical Properties, and Performance Analysis, The Evidence Lower Bound of Variational Autoencoders Converges to a Sum of Three Entropies, Geometry-Aware Hamiltonian Variational Auto-Encoder, Quaternion-Valued Variational Autoencoder, VarGrad: A Low-Variance Gradient Estimator for Variational Inference, Unsupervised Machine Learning Discovery of Chemical Transformation Pathways from Atomically-Resolved Imaging Data, Characterizing the Latent Space of Molecular Deep Generative Models with Persistent Homology Metrics, Addressing Variance Shrinkage in Variational Autoencoders using Quantile Regression, Scene Gated Social Graph: Pedestrian Trajectory Prediction Based on Dynamic Social Graphs and Scene Constraints, Anomaly Detection With Conditional Variational Autoencoders, Category-Learning with Context-Augmented Autoencoder, Bigeminal Priors Variational auto-encoder, Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains, VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models, Generation of lyrics lines conditioned on music audio clips, ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis, Discond-VAE: Disentangling Continuous Factors from the Discrete, Old Photo Restoration via Deep Latent Space Translation, DeepWriteSYN: On-Line Handwriting Synthesis via Deep Short-Term Representations, Multilinear Latent Conditioning for Generating Unseen Attribute Combinations, Ordinal-Content VAE: Isolating Ordinal-Valued Content Factors in Deep Latent Variable Models, Variational Autoencoders for Jet Simulation, Quasi-symplectic Langevin Variational Autoencoder, Exploiting Latent Codes: Interactive Fashion Product Generation, Similar Image Retrieval, and Cross-Category Recommendation using Variational Autoencoders, Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow, LaDDer: Latent Data Distribution Modelling with a Generative Prior, An Intelligent CNN-VAE Text Representation Technology Based on Text Semantics for Comprehensive Big Data, Dynamical Variational Autoencoders: A Comprehensive Review, Uncertainty-Aware Surrogate Model For Oilfield Reservoir Simulation, Game Level Clustering and Generation using Gaussian Mixture VAEs, Variational Autoencoder for Anti-Cancer Drug Response Prediction, A Systematic Assessment of Deep Learning Models for Molecule Generation, Linear Disentangled Representations and Unsupervised Action Estimation, Learning Interpretable Representation for Controllable Polyphonic Music Generation, PIANOTREE VAE: Structured Representation Learning for Polyphonic Music, Generate High Resolution Images With Generative Variational Autoencoder, Anomaly localization by modeling perceptual features, DSM-Net: Disentangled Structured Mesh Net for Controllable Generation of Fine Geometry, Dual Gaussian-based Variational Subspace Disentanglement for Visible-Infrared Person Re-Identification, Quantitative Understanding of VAE by Interpreting ELBO as Rate Distortion Cost of Transform Coding, Learning Disentangled Representations with Latent Variation Predictability, Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations, Learning the Latent Space of Robot Dynamics for Cutting Interaction Inference, Novel View Synthesis on Unpaired Data by Conditional Deformable Variational Auto-Encoder, It's LeVAsa not LevioSA!