Variational Autoencoders

Also known as VAE

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10 papers in the last four weeks, up 25% on the four weeks before. 0.1% of all new papers.

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Evolutionary Curriculum Learning Improves Biological Sequence Modeling
Aug 1, 2026Richard Zhu, Kento NishiVariational Autoencoders Curriculum Learning
Department of Computer Science, Harvard University, Cambridge, MA, USA · Department of Statistics, Harvard University, Cambridge, MA, USA.
Robust Watermarks Meet Backdoored Models: Evading Diffusion Semantic Watermarks via Stealthy Backdoor
Aug 1, 2026Jinyuan Liu, Tianshuo Cong, Pei Li +4Variational Autoencoders Diffusion Model Watermarking
1Tsinghua University · 2Shandong University · 4Shandong Key Laboratory of Artificial Intelligence Security, Shandong University +5
EgoGVAE: Ego-body Mesh Reconstruction via Guided Variational Autoencoder
Jul 30, 2026Jaehun Jung, Wonjun KimVariational Autoencoders Human Mesh Recovery
Konkuk University
Meshy T2: Fast Native Mesh Generation with Flow Matching
Jul 28, 2026Jiale Xu, Rendong Liang, Yuhao Long +5Variational Autoencoders 3D Mesh Generation
Meshy AI
Can Deep Generative Models Reproduce Non-Stationary Gaussian Random Fields?
Jul 28, 2026Daniel Kua, Yan SongVariational Autoencoders Flow Matching
Department of Statistics, University of British Columbia
P3: Probabilistic Policy Propagation for Stable VAE-Based Robot Learning
Jul 28, 2026Liyun Yan, Jianming Ma, Yang Zhang +5Variational Autoencoders Robot Policy Learning
Shanghai Jiao Tong University, Shanghai, China · Shanghai Innovation Institute, Shanghai, China · Tongji University, Shanghai, China +1
The balance between compactness and forecast accuracy of data-driven latent-space reduced-order models in controlled wake flows
Jul 27, 2026Alberto Solera-Rico, Patricia García-Caspueñas, Carlos Sanmiguel Vila +1Variational Autoencoders Latent Dynamics Modeling
Sub-directorate general of aeronautical systems, Spanish National Institute for Aerospace Technology (INTA), ctra. M-301, km. 10.5, 28330 San Mart´ın de la Vega, Madrid, Spain. · Departamento de Ingenier´ıa Aeroespacial, Universidad Carlos III de Madrid, Avenida de la Universidad 30, 28911 Legan´es, Madrid, Spain. · Department of Mechanical Engineering, University of Washington, 1410 NE Campus Parkway, Seattle, WA 98195, USA.
MSVS-VAE: Multi-Scale Anchored VecSet for High-Fidelity 3D Reconstruction
Jul 27, 2026Dehao Hao, Kaiyi Zhang, Tanghui Jia +10Variational Autoencoders 3D Reconstruction
The Hong Kong University of Science and Technology, Hong Kong SAR, China · Peking University, Beijing, China · The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China +1
OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation
Jul 26, 2026Jun Zhan, Chen Yang, Yitian Gong +23Variational Autoencoders Audio-Video Generation
1Fudan University · 2Shanghai Innovation Institute · 3MOSI Intelligence +1
Soft-Constrained Optimization of Latent Space in Variational Autoencoders
Jul 26, 2026Ye ShiVariational Autoencoders Disentangled Representation Learning
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47906 USA
A Multi-stage Constrained Optimization Framework for Data-driven Problems
Jul 26, 2026Ye ShiVariational Autoencoders Constrained Generative Modeling
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA
Gradient-Based Latent Decomposition Reveals Mechanisms of Feature Degradation in Weakly Supervised Mammography
Jul 24, 2026Vinceline Bertrand, Ionut CardeiVariational Autoencoders Hierarchical Representation Learning
Florida Atlantic University, Boca Raton, FL, USA
CASL-VAE: Learning Structured Latent Variables from Unpaired Data for Semi-supervised Clustering and Paired Sample Generation
Jul 9, 2026Sai Spandana Chintapalli, Pratik Chaudhari, Christos DavatzikosVariational Autoencoders Contrastive Learning
Center for AI and Data Science for Integrated Diagnostics, University of Pennsylvania · Department of Bioengineering, University of Pennsylvania · Department of Electrical and Systems Engineering, University of Pennsylvania +1
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
Jul 8, 2026Ishaan Batta, Meenu Ajith, Vince CalhounVariational Autoencoders Multimodal Embedding
Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
$\mathbfλ$ -VAE: Variance Equalization for Posterior Collapse
Jul 6, 2026Girum DemisseVariational Autoencoders Posterior Collapse
Microsoft African Development Center Nairobi Kenya
Uncertainty-aware damage identification in short-span bridges via physics-informed variational autoencoder
Jul 6, 2026Ana Fernandez-Navamuel, A. Javier Omella, Diego Zamora-Sanchez +1Variational Autoencoders Uncertainty Quantification
Basque Center for Applied Mathematics (BCAM), Bilbao, Spain. · Departamento de Matematica Aplicada, Universidad de Zaragoza/IUMA, Zaragoza, Spain. · TECNALIA, Basque Research and Technology Alliance (BRTA), Parque Cientiﬁco y Tecnologico de Bizkaia, Astondo bidea, Ediﬁcio 700, Derio, E-48160, Spain. +1
eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions
Jun 30, 2026Qijun Chen, Shaofan LiVariational Autoencoders Variational Inference
Department of Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA
Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry
Jun 26, 2026Mateusz Kubita, Jan Zubalewicz, Krzysztof SiwekVariational Autoencoders Autoencoders
Warsaw University of Technology Warsaw, Poland
Tailor Made Embeddings for Quantum Machine Learning
Jun 24, 2026Aldo Lamarre, Dominik ŠafránekVariational Autoencoders Quantum Machine Learning
Department of Electrical Engineering, KAIST, Daejeon, South Korea · Center for Theoretical Physics of Complex Systems, Institute for Basic Science (IBS), Daejeon - 34126, Korea · Faculty of Mathematics and Physics, Charles University, Ke Karlovu 3, 121 16 Praha 2, Czech Republic
Variational Autoencoder Layer
Jun 24, 2026Gananath RVariational Autoencoders Autoencoders
STAR-VAE: Structured Topology-Aware Regularization for Audio Reconstruction and Generation
Jun 22, 2026Huadai Liu, Wen Wang, Kaicheng Luo +3Variational Autoencoders Text-to-Audio Generation
Hong Kong University of Science and Technology · Tongyi Fun Team, Alibaba Group.
The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production
Jun 22, 2026Guilhem Fauré, Mostafa Sadeghi, Sam Bigeard +1Variational Autoencoders Representation Learning
MULTISPEECH · Universit´e de Lorraine, CNRS, Inria, LORIA · LORIA
Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images
Jun 19, 2026Jingyuan Chen, Kangrui Ruan, Junzhe ZhangVariational Autoencoders Structural Causal Models
Department of EECS Syracuse University · AWS AI Lab
Physics-Preserving Latent Compression for Zero-Shot Resolution Transfer in 3D Turbulence
Jun 19, 2026Yilong Dai, Yiming Sun, Yiheng Chen +4Variational Autoencoders Physics-Informed ML
The University of Alabama · University of Pittsburgh · University of Maryland, College Park
Gaussian Process Prior Variational Autoencoder for Endoscopic Videos
Jun 18, 2026Ivan De Boi, Xinxing Shi, Xiaoyu Jiang +5Variational Autoencoders Endoscopy
Department of Electromechanics, InViLab, University of Antwerp, Antwerp, Belgium · Department of Computer Science, University of Manchester, Manchester, United Kingdom · Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
Beyond the Smile: A Hybrid Convolutional VAE for Crypto Volatility Surfaces
Jun 15, 2026Sadanand Singh, Allam Reddy, Manan ChopraVariational Autoencoders Quantitative Finance
Jasper Research, USA
Context-Aware Markov VAE for CSI Compression in Wireless Systems
Jun 15, 2026Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan +2Variational Autoencoders Neural Network Compression
Ericsson Research, Stockholm, Sweden · Department of Computer and Information Science, Linköping University, Linköping, Sweden · Department of Electrical Engineering, Linköping University, Linköping, Sweden
Task-guided cross-subject latent alignment: a multi-encoder-decoder VAE
Jun 14, 2026Angeliki Papathanasiou, Jascha Achterberg, Thomas E. Nichols +1Variational Autoencoders Neural Decoding
Centre for Neural Circuits and Behaviour, Department of Physiology Anatomy and Genetics, University of Oxford · Big Data Institute, Nuﬃeld Department of Medicine, University of Oxford
Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
Jun 12, 2026Monika Choudhary, Xiaoya Chong, Runbo Jiang +25Variational Autoencoders Visual Representation Learning
Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised $β$ -VAE and Quantum Kernels
Jun 12, 2026Tia Tiwari, Vamshi Krishna Kancharla, Neelam SinhaVariational Autoencoders Hybrid Quantum-Classical ML
Centre for Brain Research, Indian Institute of Science · Vision and AI Lab (VAL), Indian Institute of Science

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Evolutionary Curriculum Learning Improves Biological Sequence Modeling

Richard Zhu, Kento Nishi

Department of Computer Science, Harvard University, Cambridge, MA, USA · Department of Statistics, Harvard University, Cambridge, MA, USA.

Variational Autoencoders Curriculum Learning Molecular Generation Sequence Modeling

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Variational autoencoders (VAEs) trained on multiple sequence alignments (MSAs) have emerged as powerful generative models for biological sequences, with applications ranging from disease variant prediction to functional RNA design. However, standard biological VAE training treats all sequences as exchangeable, ignoring the rich evolutionary structure that organizes homologous sequences from evolutionarily close to highly divergent. We propose Evolutionary Curriculum Learning (ECL), a training strategy that exploits this structure by progressively exposing the model to sequences of increasing evolutionary distance from sampled anchors, following a power-law expansion schedule. Applied to two architecturally distinct VAE models and two biological domains--protein variant effect prediction with EVE and RNA family sequence generation with RfamGen--ECL improves downstream task performance across five random seeds per configuration. Mean ClinVar classification AUROC rises from 0.981 to 0.989 for p53; for PTEN, ECL attains 1.000 in every seed whereas the baseline is unstable (mean 0.905, falling as low as 0.54). For RNA, ECL raises mean covariance-model bit scores on all three families tested and exceeds its seed-matched baseline in 12 of 15 training runs, though with only three families the effect cannot be established as significant at the family level. Ablation experiments show that progressively expanding the sampled sequences by evolutionary distance outperforms fixed-size neighborhood sampling in addition to uniform random sampling. Evolutionary distance is therefore a useful inductive bias for ordering the training curriculum in biological sequence modeling.

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