% Key papers for Regularizing VAE latent information (scime atlas, https://atlas.scime.ai). Authors are not included: add them from the paper pages.

@inproceedings{iclr2018_wasserstein_28533,
  title = {{Wasserstein Auto-Encoders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/182},
}

@inproceedings{neurips2020_nvae_60379,
  title = {{NVAE: A Deep Hierarchical Variational Autoencoder}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2020},
  url = {https://nips.cc/virtual/2020/poster/17497},
}

@inproceedings{iclr2019_lagging_29222,
  title = {{Lagging Inference Networks and Posterior Collapse in Variational Autoencoders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/640},
}

@inproceedings{icml2017_adversarial_36056,
  title = {{Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/671},
}

@inproceedings{neurips2017_learning_56418,
  title = {{Learning Disentangled Representations with Semi-Supervised Deep Generative Models}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2017},
  url = {https://nips.cc/virtual/2017/poster/9365},
}

@inproceedings{aaai2017_denoising_104400,
  title = {{Denoising Criterion for Variational Auto-Encoding Framework}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2017},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/10777},
}

@inproceedings{iclr2019_dialogwae_28801,
  title = {{DialogWAE: Multimodal Response Generation with Conditional Wasserstein Auto-Encoder}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/743},
}

@inproceedings{cvpr2019_variational_14994,
  title = {{Variational Autoencoders Pursue PCA Directions (by Accident)}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{cvpr2020_guided_15626,
  title = {{Guided Variational Autoencoder for Disentanglement Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020},
}

@inproceedings{iclr2020_from_29587,
  title = {{From Variational to Deterministic Autoencoders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2020},
  url = {https://openreview.net/forum?id=S1g7tpEYDS},
}

@inproceedings{icml2017_improved_36227,
  title = {{Improved Variational Autoencoders for Text Modeling using Dilated Convolutions}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2017},
  url = {https://icml.cc/virtual/2017/poster/834},
}

@inproceedings{iclr2018_on_28724,
  title = {{On Unifying Deep Generative Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2018},
  url = {https://iclr.cc/virtual/2018/poster/166},
}

@inproceedings{iclr2019_diagnosing_28737,
  title = {{Diagnosing and Enhancing VAE Models}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2019},
  url = {https://iclr.cc/virtual/2019/poster/899},
}

@inproceedings{cvpr2017_infinite_12356,
  title = {{Infinite Variational Autoencoder for Semi-Supervised Learning}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017},
}

@inproceedings{neurips2018_learning_57286,
  title = {{Learning Latent Subspaces in Variational Autoencoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11623},
}

@inproceedings{neurips2018_amortized_56861,
  title = {{Amortized Inference Regularization}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2018},
  url = {https://nips.cc/virtual/2018/poster/11434},
}

@inproceedings{neurips2019_semi_58972,
  title = {{Semi-Implicit Graph Variational Auto-Encoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14076},
}

@inproceedings{cvpr2019_a_13797,
  title = {{A Variational Auto-Encoder Model for Stochastic Point Processes}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019},
}

@inproceedings{neurips2019_continuous_58038,
  title = {{Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14209},
}

@inproceedings{neurips2019_the_59076,
  title = {{The continuous Bernoulli: fixing a pervasive error in variational autoencoders}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2019},
  url = {https://nips.cc/virtual/2019/poster/14267},
}

@inproceedings{neurips2024_qvae_72191,
  title = {{QVAE-Mole: The Quantum VAE with Spherical Latent Variable Learning for 3-D Molecule Generation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/95150},
}

@inproceedings{aaai2025_dr_8623,
  title = {{DR-VAE: Debiased and Representation-enhanced Variational Autoencoder for Collaborative Recommendation}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/33385},
}

@inproceedings{neurips2024_poisson_73184,
  title = {{Poisson Variational Autoencoder}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2024},
  url = {https://neurips.cc/virtual/2024/poster/94249},
}

@inproceedings{aaai2025_multimodal_10070,
  title = {{Multimodal Variational Autoencoder: A Barycentric View}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2025},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/34209},
}

@inproceedings{iclr2024_beyond_33632,
  title = {{Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19443},
}

@inproceedings{iclr2024_diffenc_33760,
  title = {{DiffEnc: Variational Diffusion with a Learned Encoder}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/19315},
}

@inproceedings{iclr2025_restructuring_81492,
  title = {{Restructuring Vector Quantization with the Rotation Trick}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/30284},
}

@inproceedings{iclr2024_a_34610,
  title = {{A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2024},
  url = {https://iclr.cc/virtual/2024/poster/18464},
}

@inproceedings{icml2025_neighbour_47651,
  title = {{Neighbour-Driven Gaussian Process Variational Autoencoders for Scalable Structured Latent Modelling}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44946},
}

@inproceedings{icml2025_aggregation_48316,
  title = {{Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2025},
  url = {https://icml.cc/virtual/2025/poster/44298},
}

@inproceedings{icml2026_phase_51840,
  title = {{Phase-Type Variational Autoencoders for Heavy-Tailed Data}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2026},
  url = {https://openreview.net/forum?id=QN1iaHy54s},
}

@inproceedings{neurips2025_gmm_76780,
  title = {{GMM-based VAE model with Normalising Flow for effective stochastic segmentation}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2025},
  url = {https://openreview.net/forum?id=MxYvh8zpbR},
}

@inproceedings{iclr2026_on_89091,
  title = {{On the Mechanisms of Collaborative Learning in VAE Recommenders}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=uiTUuRbFsb},
}

@inproceedings{iclr2026_discrete_89215,
  title = {{Discrete Variational Autoencoding via Policy Search}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2026},
  url = {https://openreview.net/forum?id=wJhhCmbFzY},
}

@inproceedings{aaai2026_ea_108414,
  title = {{EA-VAE: Learning to Reconstruct Dysarthric Speech via Variational Autoencoder with Encoding Alignment}},
  booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
  year = {2026},
  url = {https://ojs.aaai.org/index.php/AAAI/article/view/40766},
}

@inproceedings{icml2024_why_43698,
  title = {{Why do Variational Autoencoders Really Promote Disentanglement?}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/34754},
}

@inproceedings{icml2024_strwaes_45190,
  title = {{StrWAEs to Invariant Representations}},
  booktitle = {International Conference on Machine Learning (ICML)},
  year = {2024},
  url = {https://icml.cc/virtual/2024/poster/33262},
}

@inproceedings{iclr2025_vae_83870,
  title = {{VAE-Var: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation in Meteorology}},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year = {2025},
  url = {https://iclr.cc/virtual/2025/poster/27950},
}

@inproceedings{neurips2026_stochastic_94531,
  title = {{Stochastic Interpolants via Conditional Dependent Coupling}},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
  year = {2026},
  url = {https://neurips.cc/virtual/2026/poster/149946},
}

@inproceedings{cvpr2026_negative_96108,
  title = {{Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling}},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2026},
  url = {https://openaccess.thecvf.com/content/CVPR2026/html/Zhang_Negative_Binomial_Variational_Autoencoders_for_Overdispersed_Latent_Modeling_CVPR_2026_paper.html},
}