mlsanityData to 14 Oct 2026

Deep Gaussian Mixture Models

arXiv 1711.06929 ↗First posted 18 Nov 2017stat.ML cs.LGcited by 30

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Abstract (arXiv)

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables follow a mixture of Gaussian distributions. Thus, the deep mixture model consists of a set of nested mixtures of linear models, which globally provide a nonlinear model able to describe the data in a very flexible way. In order to avoid overparameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture thus resulting in deep mixtures of factor analysers.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Nov 2017.

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Citing papers per year: 2020: 1; 2021: 5; 2022: 2; 2023: 1; 2024: 6; 2025: 10; 2026: 5.

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  1. 2021Learned Image Compression with Gaussian-Laplacian-Logistic Mixture Model and Concatenated Residual Modulescited by 35
  2. 2025Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gatecited by 16
  3. 2025Subject Information Extraction for Novelty Detection with Domain Shiftscited by 6
  4. 2024Continual Domain Incremental Learning for Privacy-aware Digital Pathologycited by 5
  5. 2021Image Modeling with Deep Convolutional Gaussian Mixture Modelscited by 4
  6. 2024Gradient-free variational learning with conditional mixture networkscited by 4
  7. 2025Improving Environment Novelty Quantification for Effective Unsupervised Environment Designcited by 4
  8. 2025IntTrajSim: Trajectory Prediction for Simulating Multi-Vehicle driving at Signalized Intersectionscited by 3
  9. 2021Probabilistic partition of unity networks: clustering based deep approximationcited by 2
  10. 2022A new perspective on probabilistic image modelingcited by 2
  11. 2024A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale datasetcited by 1
  12. 2024SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrievalcited by 1
18 more
  1. 2025Designing Object Detection Models for TinyML: Foundations, Comparative Analysis, Challenges, and Emerging Solutionscited by 1
  2. 2025PMODE: Theoretically Grounded and Modular Mixture Modelingcited by 1
  3. 2026How to Relieve Distribution Shifts in Semantic Segmentation for Off-Road Environmentscited by 1
  4. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  5. 2026Characterizing Heterogeneous Rates in Finite Mixture Estimation via Partial Optimal Transportcited by 1
  6. 2020Tabular GANs for uneven distributioncited by 0
  7. 2021Noise-Resistant Deep Metric Learning with Probabilistic Instance Filteringcited by 0
  8. 2021Fitting large mixture models using stochastic component selectioncited by 0
  9. 2022Encoding large information structures in linear algebra and statistical modelscited by 0
  10. 2023Large-scale gradient-based training of Mixtures of Factor Analyzerscited by 0
  11. 2024Scoring rule nets: beyond mean target prediction in multivariate regressioncited by 0
  12. 2024Latent Action Priors for Locomotion with Deep Reinforcement Learningcited by 0
  13. 2025Application of Generative Adversarial Network (GAN) for Synthetic Training Data Creation to improve performance of ANN Classifier for extracting Built-Up pixels from Landsat Satellite Imagerycited by 0
  14. 2025Differentiable Expectation-Maximisation and Applications to Gaussian Mixture Model Optimal Transportcited by 0
  15. 2025Rethinking Nonlinearity: Trainable Gaussian Mixture Modules for Modern Neural Architecturescited by 0
  16. 2025Classification EM-PCA for clustering and embeddingcited by 0
  17. 2026Joint Representation Learning and Clustering via Gradient-Based Manifold Optimizationcited by 0
  18. 2026Deep Skew-t Mixture Modelscited by 0