Variational Autoencoders

ICML 2025arXiv:2502.14831

#2

Improving the Diffusability of Autoencoders

Ivan Skorokhodov, Sharath Girish, Benran Hu et al.

34

ECCV 2024arXiv:2303.12001

#3

Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry

Sai Sumedh R. Hindupur, Ekdeep S Lubana, Thomas Fel et al.

Closed-Loop Unsupervised Representation Disentanglement with $\beta$-VAE Distillation and Diffusion Probabilistic Feedback

Xin Jin, Bohan Li, Baao Xie et al.

ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

Jefferson Hernandez, Ruben Villegas, Vicente Ordonez

11

ICML 2025arXiv:2502.01567

#6

Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

Can Demircan, Tankred Saanum, Akshay Jagadish et al.

Latent Thought Models with Variational Bayes Inference-Time Computation

Deqian Kong, Minglu Zhao, Dehong Xu et al.

11

AAAI 2025arXiv:2409.09564

#8

TG-LLaVA: Text Guided LLaVA via Learnable Latent Embeddings

Dawei Yan, Pengcheng Li, Yang Li et al.

8

NeurIPS 2025arXiv:2506.15691

#9

What Do Latent Action Models Actually Learn?

Chuheng Zhang, Tim Pearce, Pushi Zhang et al.

latent action modelscontrollable changesexogenous noiseprincipal component analysis+4

7

AAAI 2024arXiv:2312.11532

#10

VAE-Var: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation in Meteorology

Yi Xiao, Qilong Jia, Kun Chen et al.

Topic-VQ-VAE: Leveraging Latent Codebooks for Flexible Topic-Guided Document Generation

YoungJoon Yoo, Jongwon Choi

topic modelinglatent codebooksvector-quantized variational autoencoderdocument generation+4

6

ICLR 2025arXiv:2409.06142

#12

Variational Search Distributions

Dan Steinberg, Rafael Oliveira, Cheng Soon Ong et al.

variational inferenceactive generationcombinatorial designsequence design+3

AAAI 2025arXiv:2412.20487

#13

Multimodal Variational Autoencoder: A Barycentric View

Peijie Qiu, Wenhui Zhu, Sayantan Kumar et al.

NeurIPS 2025arXiv:2506.15679

#14

Dense SAE Latents Are Features, Not Bugs

Xiaoqing Sun, Alessandro Stolfo, Joshua Engels et al.

ICML 2025arXiv:2507.11789

#15

Enforcing Latent Euclidean Geometry in Single-Cell VAEs for Manifold Interpolation

Alessandro Palma, Sergei Rybakov, Leon Hetzel et al.

CVPR 2024arXiv:2403.17502

#16

Latent Diffusion Models with Masked AutoEncoders

Junho Lee, Jeongwoo Shin, Hyungwook Choi et al.

Energy-Clibrated VAE with Test Time Free Lunch

Yihong Luo, Siya Qiu, Xingjian Tao et al.

SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder

Dihan Zheng, Yihang Zou, Xiaowen Zhang et al.

4

NeurIPS 2025arXiv:2410.19315

#19

High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders

Siddharth Ramchandran, Manuel Haussmann, Harri Lähdesmäki

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning

Zijian Li, Shunxing Fan, Yujia Zheng et al.

Brain-like Variational Inference

Hadi Vafaii, Dekel Galor, Jacob Yates

4

ICLR 2025arXiv:2502.19718

#22

Learning Mask Invariant Mutual Information for Masked Image Modeling

Tao Huang, Yanxiang Ma, Shan You et al.

masked image modelingmasked autoencodersself-supervised learninginformation bottleneck principle+3

4

ICLR 2024arXiv:2402.15710

#23

Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs

Xin Gao, Jian Pu

SAUCE: Selective Concept Unlearning in Vision-Language Models with Sparse Autoencoders

Jiahui Geng, Qing Li

A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data

Saptarshi Chakraborty, Peter Bartlett

3

AAAI 2025arXiv:2401.02739

#26

LV-MAE: Learning Long Video Representations through Masked-Embedding Autoencoders

Ilan Naiman, Emanuel Baruch Baruch, Oron Anschel et al.

What's in a Latent? Leveraging Diffusion Latent Space for Domain Generalization

Xavier Thomas, Deepti Ghadiyaram

Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives

Marcel Hirt, Domenico Campolo, Victoria Leong et al.

SSC-VAE: Structured Sparse Coding Based Variational Autoencoder for Detail Preserved Image Reconstruction

Hao Wang, Lu Wang, Zhongyu Wang et al.

Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors

Wasu Top Piriyakulkij, Yingheng Wang, Volodymyr Kuleshov

2

joint image-video tokenizationcausal variational autoencodervideo compression networkspatio-temporal downsampling+4

#31

High-Quality Joint Image and Video Tokenization with Causal VAE

Dawit Mureja Argaw, Xian Liu, Qinsheng Zhang et al.

ICLR 2025

1

NeurIPS 2025arXiv:2505.11029

#32

Limited Preference Data? Learning Better Reward Model with Latent Space Synthesis

Leitian Tao, Xuefeng Du, Sharon Li

Exploiting the Asymmetric Uncertainty Structure of Pre-trained VLMs on the Unit Hypersphere

Li Ju, Max Andersson, Stina Fredriksson et al.

1

NeurIPS 2025arXiv:2512.12905

#34

PAC-Bayes Bounds for Multivariate Linear Regression and Linear Autoencoders

Ruixin Guo, Ruoming Jin, Xinyu Li et al.

1

NeurIPS 2025arXiv:2506.12903

#35

Variational Learning Finds Flatter Solutions at the Edge of Stability

Avrajit Ghosh, Bai Cong, Rio Yokota et al.

variational learningedge of stabilityimplicit regularizationflat solutions+3

ICLR 2025arXiv:2409.00127

#36

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data

Phillip Si, Peng Chen

data assimilationensemble score filtersvariational autoencoderlatent representation learning+4

ICLR 2025arXiv:2410.11758

#37

Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations

Julius Aka, Johannes Brunnemann, Jörg Eiden et al.

Latent Action Pretraining from Videos

Seonghyeon Ye, Joel Jang, Byeongguk Jeon et al.

vision-language-action modelsunsupervised pretrainingaction quantizationlatent action learning+4

ICLR 2025arXiv:2506.00849

#39

Identifying latent state transitions in non-linear dynamical systems

Çağlar Hızlı, Çağatay Yıldız, Matthias Bethge et al.

Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement

Yidi Liu, Xueyang Fu, Jie Huang et al.

Imagine Beyond ! Distributionally Robust Autoencoding for State Space Coverage in Online Reinforcement Learning

Nicolas Castanet, Olivier Sigaud, Sylvain Lamprier

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

Qi Chen, Jierui Zhu, Florian Shkurti

variational autoencodersdiffusion modelsinformation-theoretic analysisencoder generalization+3

ICLR 2025arXiv:2502.04878

#43

Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems

Alan Lahoud, Erik Schaffernicht, Johannes Andreas Stork

Sparse Autoencoders Do Not Find Canonical Units of Analysis

Patrick Leask, Bart Bussmann, Michael Pearce et al.

ICLR 2025arXiv:2504.11831

#45

Sparse Autoencoders Learn Monosemantic Features in Vision-Language Models

Mateusz Pach, Shyamgopal Karthik, Quentin Bouniot et al.

Support is All You Need for Certified VAE Training

Changming Xu, Debangshu Banerjee, Deepak Vasisht et al.

variational autoencoderscertified trainingadversarial robustnesslatent layer support+3

NeurIPS 2025arXiv:2511.22853

#47

TARFVAE: Efficient One-Step Generative Time Series Forecasting via TARFLOW based VAE

Jiawen Wei, jiang lan, Pengbo Wei et al.

ICLR 2025arXiv:2409.04185

#48

Temporal-Difference Variational Continual Learning

Luckeciano Carvalho Melo, Alessandro Abate, Yarin Gal

Transformer Key-Value Memories Are Nearly as Interpretable as Sparse Autoencoders

Mengyu Ye, Jun Suzuki, Tatsuro Inaba et al.

Dimensional Collapse in VQVAEs: Evidence and Remedies

Jiayou Zhang, Yifan Shen, Guangyi Chen et al.

Residual Stream Analysis with Multi-Layer SAEs

Tim Lawson, Lucy Farnik, Conor Houghton et al.

sparse autoencoderstransformer language modelsresidual stream analysisinterpretability methods+4

ICLR 2025arXiv:2502.04730

#52

Variational Uncertainty Decomposition for In-Context Learning

I. Shavindra Jayasekera, Jacob Si, Filippo Valdettaro et al.

GMM-based VAE model with Normalising Flow for effective stochastic segmentation

Conghui Li, Chern Hong Lim, Xin Wang

PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders

Tianyu Xie, David Harry Tyensoung Richman, Jiansi Gao et al.

phylogenetic tree representationvariational autoencodersunsupervised learningtree topology generation+2

ICLR 2025arXiv:2408.08558

#55

Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm

James Cuin, Davide Carbone, O. Deniz Akyildiz

Linear combinations of latents in generative models: subspaces and beyond

Erik Bodin, Alexandru Stere, Dragos Margineantu et al.

latent variable manipulationgenerative model subspacesdiffusion modelsflow matching+4

ICLR 2025arXiv:2303.15244

#57

Manifold Learning by Mixture Models of VAEs for Inverse Problems

Giovanni S. Alberti, Johannes Hertrich, Matteo Santacesaria et al.

manifold learningvariational autoencodersmixture modelsinverse problems+3

NeurIPS 2025arXiv:2505.19470

#58

Information-theoretic Generalization Analysis for VQ-VAEs: A Role of Latent Variables

Futoshi Futami, Masahiro Fujisawa

vector-quantized vaesinformation-theoretic generalizationdiscrete latent spacesencoder-decoder models+3

ICLR 2025arXiv:2502.00754

#59

Towards Unified and Lossless Latent Space for 3D Molecular Latent Diffusion Modeling

Yanchen Luo, ZHIYUAN LIU, Yi Zhao et al.

Differentiable Causal Discovery for Latent Hierarchical Causal Models

Parjanya Prashant, Ignavier Ng, Kun Zhang et al.

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images

Aiqing Zhu, Yuting Pan, Qianxiao Li

NeurIPS 2025arXiv:2510.19640

#62

Latent Space Factorization in LoRA

Shashi Kumar, Yacouba Kaloga, John Mitros et al.

low-rank adaptationparameter-efficient finetuninglatent space factorizationvariational autoencoder+3

latent variable modelsgaussian process latent variable modelsq-exponential processsparse variational inference+3

#63

Seg-VAR:Image Segmentation with Visual Autoregressive Modeling

Rongkun Zheng, Lu Qi, Xi Chen et al.

Bayesian Regularization of Latent Representation

Chukwudi Paul Obite, Zhi Chang, Keyan Wu et al.

ICLR 2025

AAAI 2024arXiv:2310.02744

#65

Learned Image Transmission with Hierarchical Variational Autoencoder

Guangyi Zhang, Hanlei Li, Yunlong Cai et al.

Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms

Junyong Lee, Jeihee Cho, Shiho Kim

DR-VAE: Debiased and Representation-enhanced Variational Autoencoder for Collaborative Recommendation

Fan Wang, Chaochao Chen, Weiming Liu et al.

Distribution-Conditioned Adversarial Variational Autoencoder for Valid Instrumental Variable Generation

Enhancing Evolving Domain Generalization through Dynamic Latent Representations

Binghui Xie, Yongqiang Chen, Jiaqi Wang et al.

Conditional Variational Autoencoder for Sign Language Translation with Cross-Modal Alignment

Rui Zhao, Liang Zhang, Biao Fu et al.

MorphVAE: Advancing Morphological Design of Voxel-Based Soft Robots with Variational Autoencoders

Junru Song, Yang Yang, Wei Peng et al.

Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation

Jiaming Liu, Ran Xu, Senqiao Yang et al.

A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs

Sikun Yang, Hongyuan Zha

Efficient Dictionary Learning with Switch Sparse Autoencoders

Anish Mudide, Josh Engels, Eric Michaud et al.

Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis

Weiwei Lin, Chenhang HE

Community-Aware Variational Autoencoder for Continuous Dynamic Networks

Junwei Cheng, Chaobo He, Pengxing Feng et al.

Restructuring Vector Quantization with the Rotation Trick

Christopher Fifty, Ronald Junkins, Dennis Duan et al.

Cumulative Difference Learning VAE for Time-Series with Temporally Correlated Inflow-Outflow

Tianchun Li, Chengxiang Wu, Pengyi Shi et al.

SALSA: Semantically-Aware Latent Space Autoencoder

Kathryn Kirchoff, Travis Maxfield, Alexander Tropsha et al.

molecular representation learningsmiles sequence processinggraph-to-graph similaritycontrastive learning+4

ICLR 2024arXiv:2312.01133

#80

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders

Rogelio A. Mancisidor, Robert Jenssen, Shujian Yu et al.

$\alpha$TC-VAE: On the relationship between Disentanglement and Diversity

Cristian Meo, Louis Mahon, Anirudh Goyal et al.

$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence

Juno Kim, Jaehyuk Kwon, Mincheol Cho et al.

variational autoencodersdisentangled representation learninglatent space rotationorthogonality properties+3

#83

A Variational Framework for Improving Naturalness in Generative Spoken Language Models

Li-Wei Chen, Takuya Higuchi, Zakaria Aldeneh et al.

Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders

Emanuele Palumbo, Laura Manduchi, Sonia Laguna et al.

Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data

Rabia Gondur, Usama Bin Sikandar, Evan Schaffer et al.

Why do Variational Autoencoders Really Promote Disentanglement?

Pratik Bhowal, Achint Soni, Sirisha Rambhatla

ICML 2024

ICLR 2024arXiv:2310.19789

#87

Censor Dependent Variational Inference

Chuanhui Liu, Xiao Wang

DiffEnc: Variational Diffusion with a Learned Encoder

Beatrix M. G. Nielsen, Anders Christensen, Andrea Dittadi et al.

ICML 2025arXiv:2507.03910

#89

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces

Henry Moss, Sebastian Ober, Tom Diethe

manifold learningautoencoder regularizationgraph laplacianriemannian geometry+3

#90

Graph Geometry-Preserving Autoencoders

Jungbin Lim, Jihwan Kim, Yonghyeon Lee et al.

ICML 2024

ICML 2025arXiv:2506.04859

#91

Sparse Autoencoders, Again?

Yin Lu, Xuening Zhu, Tong He et al.

ICML 2025arXiv:2410.13928

#92

Epsilon-VAE: Denoising as Visual Decoding

Long Zhao, Sanghyun Woo, Ziyu Wan et al.

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting

Xingjian Wu, Xiangfei Qiu, Hongfan Gao et al.

Learning to Quantize for Training Vector-Quantized Networks

Peijia Qin, Jianguo Zhang

Automatically Interpreting Millions of Features in Large Language Models

Gonçalo Paulo, Alex Mallen, Caden Juang et al.

wasserstein autoencodersinvariant representation learningconditional independence constraintsgenerative modeling+3

#96

StrWAEs to Invariant Representations

Hyunjong Lee, Yedarm Seong, Sungdong Lee et al.

ICML 2024

ICML 2025arXiv:2502.03444

#97

Invariance-based Learning of Latent Dynamics

Kai Lagemann, Christian Lagemann, Sach Mukherjee

Masked Autoencoders Are Effective Tokenizers for Diffusion Models

Hao Chen, Yujin Han, Fangyi Chen et al.

#99

Variance as a Catalyst: Efficient and Transferable Semantic Erasure Adversarial Attack for Customized Diffusion Models

Jiachen Yang, Yusong Wang, Yanmei Fang et al.

DDMI: Domain-agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

Dogyun Park, Sihyeon Kim, Sojin Lee et al.

ICLR 2024