{"total":24,"rows":[{"id":"2403.00025","title":"On the Challenges and Opportunities in Generative AI","created":"2024-02-28","cited_by_count":31,"area":null},{"id":"2403.05300","title":"Unity by Diversity: Improved Representation Learning in Multimodal VAEs","created":"2024-03-08","cited_by_count":14,"area":null},{"id":"2303.12706","title":"Multi-modal Variational Autoencoders for normative modelling across multiple imaging modalities","created":"2023-03-16","cited_by_count":10,"area":null},{"id":"2508.03278","title":"Artificial Intelligence and Generative Models for Materials Discovery -- A Review","created":"2025-08-05","cited_by_count":8,"area":null},{"id":"2305.03582","title":"A multimodal dynamical variational autoencoder for audiovisual speech representation learning","created":"2023-05-05","cited_by_count":6,"area":null},{"id":"2401.11631","title":"Text-to-Image Cross-Modal Generation: A Systematic Review","created":"2024-01-21","cited_by_count":5,"area":null},{"id":"2509.23548","title":"Disentanglement of Variations with Multimodal Generative Modeling","created":"2025-09-28","cited_by_count":4,"area":null},{"id":"2312.00992","title":"Improving Normative Modeling for Multi-modal Neuroimaging Data using mixture-of-product-of-experts variational autoencoders","created":"2023-12-02","cited_by_count":3,"area":null},{"id":"2505.01134","title":"Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders","created":"2025-05-02","cited_by_count":3,"area":null},{"id":"2209.03048","title":"Benchmarking Multimodal Variational Autoencoders: CdSprites+ Dataset and Toolkit","created":"2022-09-07","cited_by_count":2,"area":null},{"id":"2408.09576","title":"A Markov Random Field Multi-Modal Variational AutoEncoder","created":"2024-08-18","cited_by_count":2,"area":null},{"id":"2305.11832","title":"Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis","created":"2023-05-19","cited_by_count":1,"area":null},{"id":"2305.19726","title":"Learning Representations without Compositional Assumptions","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2401.02141","title":"Bayesian Unsupervised Disentanglement of Anatomy and Geometry for Deep Groupwise Image Registration","created":"2024-01-04","cited_by_count":1,"area":null},{"id":"2403.07456","title":"A tutorial on multi-view autoencoders using the multi-view-AE library","created":"2024-03-12","cited_by_count":1,"area":null},{"id":"2508.17376","title":"ShaLa: Multimodal Shared Latent Space Modelling","created":"2025-08-24","cited_by_count":1,"area":null},{"id":"2603.29812","title":"AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2410.11403","title":"Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference","created":"2024-10-15","cited_by_count":0,"area":null},{"id":"2411.03097","title":"Correlating Variational Autoencoders Natively For Multi-View Imputation","created":"2024-11-05","cited_by_count":0,"area":null},{"id":"2506.10265","title":"Ground Reaction Force Estimation via Time-aware Knowledge Distillation","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2508.11180","title":"No Data Wasted: A Semi-supervised Generative Model for Incomplete Multi-view Data Integration with Missing Labels","created":"2025-08-15","cited_by_count":0,"area":null},{"id":"2604.27613","title":"AMGenC: Generating Charge Balanced Amorphous Materials","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2605.00644","title":"Learning Multimodal Energy-Based Model with Multimodal Variational Auto-Encoder via MCMC Revision","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2606.13381","title":"H\\\"older++: Improving the Quality-Coherence Trade-off in Multimodal VAEs","created":"2026-06-11","cited_by_count":0,"area":null}],"next_cursor":null}