{"total":282,"rows":[{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252,"area":null},{"id":"2107.10912","title":"Explainable artificial intelligence (XAI) in deep learning-based medical image analysis","created":"2021-07-22","cited_by_count":147,"area":null},{"id":"2108.08827","title":"ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis","created":"2021-08-19","cited_by_count":122,"area":null},{"id":"2210.00580","title":"GFlowNets and variational inference","created":"2022-10-02","cited_by_count":106,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2307.10894","title":"Human Motion Generation: A Survey","created":"2023-07-20","cited_by_count":96,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2202.06533","title":"An Introduction to Neural Data Compression","created":"2022-02-14","cited_by_count":79,"area":null},{"id":"2103.15619","title":"SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data","created":"2021-03-29","cited_by_count":78,"area":null},{"id":"2103.04174","title":"Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction","created":"2021-03-06","cited_by_count":75,"area":null},{"id":"2112.05130","title":"Multimodal Conditional Image Synthesis with Product-of-Experts GANs","created":"2021-12-09","cited_by_count":75,"area":null},{"id":"2211.15979","title":"AirFormer: Predicting Nationwide Air Quality in China with Transformers","created":"2022-11-29","cited_by_count":73,"area":null},{"id":"2201.07189","title":"MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory Prediction","created":"2022-01-18","cited_by_count":59,"area":null},{"id":"2102.08248","title":"Hierarchical VAEs Know What They Don't Know","created":"2021-02-16","cited_by_count":58,"area":null},{"id":"2306.15764","title":"High Fidelity Image Counterfactuals with Probabilistic Causal Models","created":"2023-06-27","cited_by_count":53,"area":null},{"id":"2108.12043","title":"Learning Disentangled Representations in the Imaging Domain","created":"2021-08-26","cited_by_count":46,"area":null},{"id":"2102.09532","title":"Clockwork Variational Autoencoders","created":"2021-02-18","cited_by_count":42,"area":null},{"id":"2203.07586","title":"Long Document Summarization with Top-down and Bottom-up Inference","created":"2022-03-15","cited_by_count":42,"area":null},{"id":"2105.04090","title":"MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE","created":"2021-05-10","cited_by_count":40,"area":null},{"id":"2206.03066","title":"Recent Advances for Quantum Neural Networks in Generative Learning","created":"2022-06-07","cited_by_count":39,"area":null},{"id":"2401.02013","title":"SwitchTab: Switched Autoencoders Are Effective Tabular Learners","created":"2024-01-04","cited_by_count":38,"area":null},{"id":"2106.03630","title":"Efficient Iterative Amortized Inference for Learning Symmetric and Disentangled Multi-Object Representations","created":"2021-06-07","cited_by_count":36,"area":null},{"id":"2103.16146","title":"Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation","created":"2021-03-30","cited_by_count":35,"area":null},{"id":"2208.13056","title":"Lossy Image Compression with Quantized Hierarchical VAEs","created":"2022-08-27","cited_by_count":35,"area":null},{"id":"2208.14153","title":"Identifying Weight-Variant Latent Causal Models","created":"2022-08-30","cited_by_count":34,"area":null},{"id":"2303.01274","title":"Measuring axiomatic soundness of counterfactual image models","created":"2023-03-02","cited_by_count":34,"area":null},{"id":"2302.08899","title":"QARV: Quantization-Aware ResNet VAE for Lossy Image Compression","created":"2023-02-16","cited_by_count":32,"area":null},{"id":"2106.05241","title":"Multi-Facet Clustering Variational Autoencoders","created":"2021-06-09","cited_by_count":28,"area":null},{"id":"2106.12271","title":"Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoders","created":"2021-06-23","cited_by_count":28,"area":null},{"id":"2401.00365","title":"HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes","created":"2023-12-31","cited_by_count":27,"area":null},{"id":"2104.01374","title":"Interpretable Unsupervised Diversity Denoising and Artefact Removal","created":"2021-04-03","cited_by_count":26,"area":null},{"id":"2203.13751","title":"Efficient-VDVAE: Less is more","created":"2022-03-25","cited_by_count":26,"area":null},{"id":"2102.12037","title":"Conditional Image Generation by Conditioning Variational Auto-Encoders","created":"2021-02-24","cited_by_count":25,"area":null},{"id":"2106.05251","title":"Bayesian Attention Belief Networks","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2209.02606","title":"Unifying Generative Models with GFlowNets and Beyond","created":"2022-09-06","cited_by_count":24,"area":null},{"id":"2107.02757","title":"Sawtooth Factorial Topic Embeddings Guided Gamma Belief Network","created":"2021-06-30","cited_by_count":23,"area":null},{"id":"2301.12458","title":"SeeGera: Self-supervised Semi-implicit Graph Variational Auto-encoders with Masking","created":"2023-01-29","cited_by_count":23,"area":null},{"id":"2101.07496","title":"Disentangled Recurrent Wasserstein Autoencoder","created":"2021-01-19","cited_by_count":22,"area":null},{"id":"2310.11011","title":"From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modeling","created":"2023-10-17","cited_by_count":22,"area":null},{"id":"2403.20287","title":"Benchmarking Counterfactual Image Generation","created":"2024-03-29","cited_by_count":22,"area":null},{"id":"2103.03158","title":"A Structural Causal Model for MR Images of Multiple Sclerosis","created":"2021-03-04","cited_by_count":21,"area":null},{"id":"2109.04999","title":"Fairness without the sensitive attribute via Causal Variational Autoencoder","created":"2021-09-10","cited_by_count":21,"area":null},{"id":"2207.06130","title":"Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation","created":"2022-07-13","cited_by_count":20,"area":null},{"id":"2202.04599","title":"Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo","created":"2022-02-09","cited_by_count":19,"area":null},{"id":"2204.10090","title":"Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach","created":"2022-04-21","cited_by_count":18,"area":null},{"id":"2312.01133","title":"$t^3$-Variational Autoencoder: Learning Heavy-tailed Data with Student's t and Power Divergence","created":"2023-12-02","cited_by_count":18,"area":null},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2406.00519","title":"Learning Discrete Concepts in Latent Hierarchical Models","created":"2024-06-01","cited_by_count":17,"area":null},{"id":"2204.01565","title":"HiT-DVAE: Human Motion Generation via Hierarchical Transformer Dynamical VAE","created":"2022-04-04","cited_by_count":16,"area":null},{"id":"2209.10888","title":"Amortized Variational Inference: A Systematic Review","created":"2022-09-22","cited_by_count":16,"area":null},{"id":"2310.05718","title":"EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders","created":"2023-10-09","cited_by_count":16,"area":null},{"id":"2102.08663","title":"Preventing Oversmoothing in VAE via Generalized Variance Parameterization","created":"2021-02-17","cited_by_count":15,"area":null},{"id":"2205.00608","title":"Physics-aware Reduced-order Modeling of Transonic Flow via $\\beta$-Variational Autoencoder","created":"2022-05-02","cited_by_count":15,"area":null},{"id":"2210.10625","title":"HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding","created":"2022-10-16","cited_by_count":15,"area":null},{"id":"2310.19272","title":"NPCL: Neural Processes for Uncertainty-Aware Continual Learning","created":"2023-10-30","cited_by_count":14,"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":"2203.09303","title":"MSPred: Video Prediction at Multiple Spatio-Temporal Scales with Hierarchical Recurrent Networks","created":"2022-03-17","cited_by_count":13,"area":null},{"id":"2309.08747","title":"Unified Brain MR-Ultrasound Synthesis using Multi-Modal Hierarchical Representations","created":"2023-09-15","cited_by_count":13,"area":null},{"id":"2102.05185","title":"Benchmarks, Algorithms, and Metrics for Hierarchical Disentanglement","created":"2021-02-09","cited_by_count":12,"area":null},{"id":"2102.06431","title":"VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention","created":"2021-02-12","cited_by_count":12,"area":null},{"id":"2102.09310","title":"VAE Approximation Error: ELBO and Exponential Families","created":"2021-02-18","cited_by_count":12,"area":null},{"id":"2105.04046","title":"A likelihood approach to nonparametric estimation of a singular distribution using deep generative models","created":"2021-05-09","cited_by_count":12,"area":null},{"id":"2109.02490","title":"Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models","created":"2021-09-06","cited_by_count":12,"area":null},{"id":"2202.10951","title":"Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximations","created":"2022-02-22","cited_by_count":12,"area":null},{"id":"2210.17415","title":"ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images","created":"2022-10-27","cited_by_count":12,"area":null},{"id":"2106.13746","title":"On Incorporating Inductive Biases into VAEs","created":"2021-06-25","cited_by_count":11,"area":null},{"id":"2203.09445","title":"Image Super-Resolution With Deep Variational Autoencoders","created":"2022-03-17","cited_by_count":11,"area":null},{"id":"2301.08883","title":"Versatile Neural Processes for Learning Implicit Neural Representations","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2306.15166","title":"Constraining Generative Models for Engineering Design with Negative Data","created":"2023-06-27","cited_by_count":11,"area":null},{"id":"2405.14473","title":"Poisson Variational Autoencoder","created":"2024-05-23","cited_by_count":11,"area":null},{"id":"2104.05915","title":"Revisiting Bayesian Autoencoders with MCMC","created":"2021-04-13","cited_by_count":10,"area":null},{"id":"2203.09940","title":"Alleviating Adversarial Attacks on Variational Autoencoders with MCMC","created":"2022-03-18","cited_by_count":10,"area":null},{"id":"2306.08984","title":"Tree Variational Autoencoders","created":"2023-06-15","cited_by_count":10,"area":null},{"id":"2403.17410","title":"On permutation-invariant neural networks","created":"2024-03-26","cited_by_count":10,"area":null},{"id":"2406.05000","title":"AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation","created":"2024-06-07","cited_by_count":10,"area":null},{"id":"2110.14286","title":"TopicNet: Semantic Graph-Guided Topic Discovery","created":"2021-10-27","cited_by_count":9,"area":null},{"id":"2203.01829","title":"A Brief Overview of Unsupervised Neural Speech Representation Learning","created":"2022-03-01","cited_by_count":9,"area":null},{"id":"2301.08187","title":"A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs","created":"2023-01-19","cited_by_count":9,"area":null},{"id":"2309.17182","title":"RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations","created":"2023-09-29","cited_by_count":9,"area":null},{"id":"2410.19378","title":"Unified Cross-Modal Medical Image Synthesis with Hierarchical Mixture of Product-of-Experts","created":"2024-10-25","cited_by_count":9,"area":null},{"id":"2107.04197","title":"REX: Revisiting Budgeted Training with an Improved Schedule","created":"2021-07-09","cited_by_count":8,"area":null},{"id":"2303.07048","title":"Hybrid Variational Autoencoder for Time Series Forecasting","created":"2023-03-13","cited_by_count":8,"area":null},{"id":"2305.14651","title":"Revisit and Outstrip Entity Alignment: A Perspective of Generative Models","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2306.06323","title":"Learning Joint Latent Space EBM Prior Model for Multi-layer Generator","created":"2023-06-10","cited_by_count":8,"area":null},{"id":"2310.18713","title":"Episodic Multi-Task Learning with Heterogeneous Neural Processes","created":"2023-10-28","cited_by_count":8,"area":null},{"id":"2412.01373","title":"Hierarchical VAE with a Diffusion-based VampPrior","created":"2024-12-02","cited_by_count":8,"area":null},{"id":"2103.06701","title":"Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks","created":"2021-03-10","cited_by_count":7,"area":null},{"id":"2209.07007","title":"Gromov-Wasserstein Autoencoders","created":"2022-09-15","cited_by_count":7,"area":null},{"id":"2304.12770","title":"Controlling Posterior Collapse by an Inverse Lipschitz Constraint on the Decoder Network","created":"2023-04-25","cited_by_count":7,"area":null},{"id":"2306.05023","title":"Beyond Vanilla Variational Autoencoders: Detecting Posterior Collapse in Conditional and Hierarchical Variational Autoencoders","created":"2023-06-08","cited_by_count":7,"area":null},{"id":"2402.01408","title":"Counterfactual Concept Bottleneck Models","created":"2024-02-02","cited_by_count":7,"area":null},{"id":"2406.06165","title":"Generalized Nested Latent Variable Models for Lossy Coding applied to Wind Turbine Scenarios","created":"2024-06-10","cited_by_count":7,"area":null},{"id":"2409.19862","title":"Learning Multimodal Latent Generative Models with Energy-Based Prior","created":"2024-09-30","cited_by_count":7,"area":null},{"id":"2106.13739","title":"Re-parameterizing VAEs for stability","created":"2021-06-25","cited_by_count":6,"area":null},{"id":"2110.12279","title":"SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation","created":"2021-10-23","cited_by_count":6,"area":null},{"id":"2110.15811","title":"CVAD: A generic medical anomaly detector based on Cascade VAE","created":"2021-10-29","cited_by_count":6,"area":null},{"id":"2209.15514","title":"Cooperation in the Latent Space: The Benefits of Adding Mixture Components in Variational Autoencoders","created":"2022-09-30","cited_by_count":6,"area":null},{"id":"2302.04855","title":"Trading Information between Latents in Hierarchical Variational Autoencoders","created":"2023-02-09","cited_by_count":6,"area":null},{"id":"2303.11217","title":"Inverse problem regularization with hierarchical variational autoencoders","created":"2023-03-20","cited_by_count":6,"area":null},{"id":"2306.14680","title":"A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy","created":"2023-06-26","cited_by_count":6,"area":null}],"next_cursor":"100"}