Key papers

• PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesis (2026)
  Ren Takahashi et al.
  5.01
• Latent graph encoding of multimodal neuroimaging features with generative AI architectures (2026)
  Ishaan Batta et al.
  4.39
• Federated Low-Rank Koopman Learning for Multivariate Time-Series Anomaly Detection in IoT Systems (2026)
  Tung-Anh Nguyen et al.
  4.39
• A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles (2026)
  S. M. Abtahiul Alam et al.
  4.39
• Multi-layer MIMO Relay as Deep Physical Neural Networks: Power Amplifiers as Activation Functions (2026)
  Meng Hua et al.
  4.39
• Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions (2026)
  Ahmad BahooToroody et al.
  4.39
• Fast Wave-optics Rendering of Multiplane Images for 3D Holographic Displays (2026)
  Brian Chao et al.
  4.39
• FE-MCFormer: a novel time-frequency interpretable architecture for machinery fault diagnosis under strong noise environments (2025)
  Yuhan Yuan et al.
  2.82
• Flow Autoencoders are Effective Protein Tokenizers (2025)
  Rohit Dilip et al.
  1.50
• Beyond-Diagonal RIS Under Non-Idealities: Learning-Based Architecture Discovery and Optimization (2025)
  Binggui Zhou et al.
  1.50
• Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation (2025)
  Md Niaz Imtiaz et al.
  1.17
• Compositional Model based Fisher Vector Coding for Image Classification (2016)
  Lingqiao Liu et al.
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• Ladder Variational Autoencoders (2016)
  Casper Kaae S{\o}nderby et al.
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• Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series (2016)
  Maximilian Soelch et al.
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• A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features (2016)
  Tim Salimans
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• Variational Autoencoders for Semi-supervised Text Classification (2016)
  Weidi Xu et al.
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• Gaussian Copula Variational Autoencoders for Mixed Data (2016)
  Suwon Suh and Seungjin Choi
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• Towards Conceptual Compression (2016)
  Karol Gregor et al.
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• Stick-Breaking Variational Autoencoders (2016)
  Eric Nalisnick and Padhraic Smyth
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• Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data (2016)
  Maximilian Karl et al.
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• Tutorial on Variational Autoencoders (2016)
  Carl Doersch
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• Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model (2016)
  Fariba Yousefi et al.
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• Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units (2016)
  Stefanos Eleftheriadis et al.
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• Inverting Variational Autoencoders for Improved Generative Accuracy (2016)
  Ian Gemp et al.
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• Discrete Variational Autoencoders (2016)
  Jason Tyler Rolfe
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• Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016)
  Yunchen Pu et al.
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• Deep Feature Consistent Variational Autoencoder (2016)
  Xianxu Hou et al.
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• Deep Variational Canonical Correlation Analysis (2016)
  Weiran Wang et al.
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• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders (2016)
  Nat Dilokthanakul et al.
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• Variational Lossy Autoencoder (2016)
  Xi Chen et al.
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• PixelVAE: A Latent Variable Model for Natural Images (2016)
  Ishaan Gulrajani et al.
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• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016)
  Zhuxi Jiang et al.
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• Variational Graph Auto-Encoders (2016)
  Thomas N. Kipf et al.
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• Inducing Interpretable Representations with Variational Autoencoders (2016)
  N. Siddharth and Brooks Paige and Alban Desmaison and Jan-Willem Van de Meent and Frank Wood and Noah D. Goodman and Pushmeet Kohli and Philip H.S. Torr
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• Infinite Variational Autoencoder for Semi-Supervised Learning (2016)
  Ehsan Abbasnejad et al.
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• Improving Variational Auto-Encoders using Householder Flow (2016)
  Jakub M. Tomczak and Max Welling
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• Adversarial Images for Variational Autoencoders (2016)
  Pedro Tabacof et al.
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• Piecewise Latent Variables for Neural Variational Text Processing (2016)
  Iulian V. Serban et al.
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• Disentangling Space and Time in Video with Hierarchical Variational Auto-encoders (2016)
  Will Grathwohl et al.
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• Variational Dropout Sparsifies Deep Neural Networks (2017)
  Dmitry Molchanov et al.
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• Deep Kernelized Autoencoders (2017)
  Michael Kampffmeyer et al.
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• Coarse Grained Exponential Variational Autoencoders (2017)
  Ke Sun et al.
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• Improved Variational Autoencoders for Text Modeling using Dilated Convolutions (2017)
  Zichao Yang et al.
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• Grammar Variational Autoencoder (2017)
  Matt J. Kusner et al.
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• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders (2017)
  Sebastian Johann Wetzel
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• Efficient variational Bayesian neural network ensembles for outlier detection (2017)
  Nick Pawlowski et al.
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• The Use of Autoencoders for Discovering Patient Phenotypes (2017)
  Harini Suresh et al.
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• Nonparametric Variational Auto-encoders for Hierarchical Representation Learning (2017)
  Prasoon Goyal et al.
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• Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders (2017)
  Tiancheng Zhao et al.
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• Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction (2017)
  Chunting Zhou and Graham Neubig
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• Reinterpreting Importance-Weighted Autoencoders (2017)
  Chris Cremer et al.
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• KATE: K-Competitive Autoencoder for Text (2017)
  Yu Chen and Mohammed J. Zaki
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• Spatial Variational Auto-Encoding via Matrix-Variate Normal Distributions (2017)
  Zhengyang Wang et al.
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• VAE with a VampPrior (2017)
  Jakub M. Tomczak and Max Welling
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• Multi-Level Variational Autoencoder: Learning Disentangled Representations from Grouped Observations (2017)
  Diane Bouchacourt et al.
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• Investigation of Using VAE for i-Vector Speaker Verification (2017)
  Timur Pekhovsky et al.
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• Learning Disentangled Representations with Semi-Supervised Deep Generative Models (2017)
  N. Siddharth et al.
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• InfoVAE: Information Maximizing Variational Autoencoders (2017)
  Shengjia Zhao et al.
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• Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow (2017)
  Jakub M. Tomczak and Max Welling
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• Tackling Over-pruning in Variational Autoencoders (2017)
  Serena Yeung et al.
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