mlsanityData to 7 Oct 2026

Stochastic Backpropagation and Approximate Inference in Deep Generative Models

arXiv 1401.4082 ↗First posted 16 Jan 2014stat.ML cs.AI cs.LGcited by 30

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Abstract (arXiv)

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent approximate posterior distributions, and that acts as a stochastic encoder of the data. We develop stochastic back-propagation -- rules for back-propagation through stochastic variables -- and use this to develop an algorithm that allows for joint optimisation of the parameters of both the generative and recognition model. We demonstrate on several real-world data sets that the model generates realistic samples, provides accurate imputations of missing data and is a useful tool for high-dimensional data visualisation.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2014.

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Citing papers per year: 2021: 13; 2022: 6; 2023: 7; 2024: 0; 2025: 1; 2026: 3.

30 papers.

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  1. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
  2. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  3. 2021Compressive Visual Representationscited by 41
  4. 2021Revitalizing CNN Attentions via Transformers in Self-Supervised Visual Representation Learningcited by 34
  5. 2021Sawtooth Factorial Topic Embeddings Guided Gamma Belief Networkcited by 23
  6. 2022Unsupervised Visual Representation Learning by Synchronous Momentum Groupingcited by 22
  7. 2023Soft Neighbors are Positive Supporters in Contrastive Visual Representation Learningcited by 18
  8. 2021Entropic Issues in Likelihood-Based OOD Detectioncited by 16
  9. 2023DEFT: Dexterous Fine-Tuning for Real-World Hand Policiescited by 15
  10. 2026VJEPA: Variational Joint Embedding Predictive Architectures as Probabilistic World Modelscited by 14
  11. 2022Multiple Importance Sampling ELBO and Deep Ensembles of Variational Approximationscited by 12
  12. 2023Object-Centric Learning with Slot Mixture Modulecited by 11
18 more
  1. 2022Generalization Gap in Amortized Inferencecited by 10
  2. 2021TopicNet: Semantic Graph-Guided Topic Discoverycited by 9
  3. 2022Vitruvio: 3D Building Meshes via Single Perspective Sketchescited by 5
  4. 2022CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesiscited by 4
  5. 2025In-Context Parametric Inference: Point or Distribution Estimators?cited by 4
  6. 2021Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesiscited by 3
  7. 2021The use of Generative Adversarial Networks to characterise new physics in multi-lepton final states at the LHCcited by 3
  8. 2022Learning Invariances with Generalised Input-Convex Neural Networkscited by 3
  9. 2021toon2real: Translating Cartoon Images to Realistic Imagescited by 1
  10. 2021Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Taskcited by 1
  11. 2023Learning to sample in Cartesian MRIcited by 1
  12. 2026A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Modelscited by 1
  13. 2021Deep Probabilistic Graphical Modelingcited by 0
  14. 2021Topographic VAEs learn Equivariant Capsulescited by 0
  15. 2023Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methodscited by 0
  16. 2023Unsupervised Text Style Transfer with Deep Generative Modelscited by 0
  17. 2023The Counterattack of CNNs in Self-Supervised Learning: Larger Kernel Size might be All You Needcited by 0
  18. 2026QuanVI: Score-based Variational Inference via Quantum Maximally Mixed Statescited by 0