mlsanityData to 14 Oct 2026

Learning Stochastic Recurrent Networks

arXiv 1411.7610 ↗First posted 27 Nov 2014stat.ML cs.LGcited by 60

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Abstract (arXiv)

Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a reliable estimator of the marginal likelihood and iv) is a generalisation of deterministic recurrent neural networks. We evaluate the method on four polyphonic musical data sets and motion capture data.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2014.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 21; 2022: 17; 2023: 8; 2024: 5; 2025: 7; 2026: 1.

60 papers.

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  1. 2022Diffusion Probabilistic Modeling for Video Generationcited by 238
  2. 2021Modeling Irregular Time Series with Continuous Recurrent Unitscited by 103
  3. 2022SocialVAE: Human Trajectory Prediction using Timewise Latentscited by 67
  4. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  5. 2022Deep Latent State Space Models for Time-Series Generationcited by 38
  6. 2023Deep networks for system identification: a Surveycited by 37
  7. 2023Uncertainty-aware State Space Transformer for Egocentric 3D Hand Trajectory Forecastingcited by 34
  8. 2021Unsupervised Speech Enhancement using Dynamical Variational Auto-Encoderscited by 28
  9. 2021Bayesian Attention Belief Networkscited by 25
  10. 2022Conditional Motion In-betweeningcited by 25
  11. 2024MADiff: Motion-Aware Mamba Diffusion Models for Hand Trajectory Prediction on Egocentric Videoscited by 18
  12. 2021RNN with Particle Flow for Probabilistic Spatio-temporal Forecastingcited by 16
38 more
  1. 2021CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Marketscited by 15
  2. 2021Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Modelscited by 14
  3. 2021Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertaintycited by 11
  4. 2021A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Predictioncited by 10
  5. 2022Low-Rank Constraints for Fast Inference in Structured Modelscited by 8
  6. 2021Innovations Autoencoder and its Application in One-class Anomalous Sequence Detectioncited by 7
  7. 2023Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applicationscited by 7
  8. 2021VDSM: Unsupervised Video Disentanglement with State-Space Modeling and Deep Mixtures of Expertscited by 6
  9. 2021Policy Gradients Incorporating the Futurecited by 6
  10. 2022Competency Assessment for Autonomous Agents using Deep Generative Modelscited by 6
  11. 2023Continuous-time Autoencoders for Regular and Irregular Time Series Imputationcited by 6
  12. 2019Hierarchical Variational Imitation Learning of Control Programscited by 5
  13. 2022Phi-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilationcited by 5
  14. 2024A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training datacited by 5
  15. 2023Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenescited by 4
  16. 2021Deep Stochastic Volatility Modelcited by 2
  17. 2022Learning Deep Input-Output Stable Dynamicscited by 2
  18. 2022State-Regularized Recurrent Neural Networks to Extract Automata and Explain Predictionscited by 2
  19. 2024Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systemscited by 2
  20. 2025Deep Belief Markov Models for POMDP Inferencecited by 2
  21. 2021Stochastic Recurrent Neural Network for Multistep Time Series Forecastingcited by 1
  22. 2021Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Seriescited by 1
  23. 2021Deep Neural Networks and End-to-End Learning for Audio Compressioncited by 1
  24. 2021Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systemscited by 1
  25. 2021Regularized Sequential Latent Variable Models with Adversarial Neural Networkscited by 1
  26. 2022Neural Generalised AutoRegressive Conditional Heteroskedasticitycited by 1
  27. 2022Flat Latent Manifolds for Human-machine Co-creation of Musiccited by 1
  28. 2022Benchmarking Generative Latent Variable Models for Speechcited by 1
  29. 2022Wildfire Forecasting with Satellite Images and Deep Generative Modelcited by 1
  30. 2023Learning Mixture Structure on Multi-Source Time Series for Probabilistic Forecastingcited by 1
  31. 2023Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Datacited by 1
  32. 2025Towards Foundation Auto-Encoders for Time-Series Anomaly Detectioncited by 1
  33. 2025FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Predictioncited by 1
  34. 2025Double projection for reconstructing dynamical systems: between stochastic and deterministic regimescited by 1
  35. 2026SELDON: Supernova Explosions Learned by Deep ODE Networkscited by 1
  36. 2021Learning deep autoregressive models for hierarchical datacited by 0
  37. 2021Switching Recurrent Kalman Networkscited by 0
  38. 2021Estimating the Value-at-Risk by Temporal VAEcited by 0

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